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BEGIN:VEVENT
SUMMARY:Christophe Biscio (Aalborg University)
DTSTART;VALUE=DATE-TIME:20220929T131500Z
DTEND;VALUE=DATE-TIME:20220929T140000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/1
DESCRIPTION:Title: Asymptotic topological data analysis for point processes\nby Christop
he Biscio (Aalborg University) as part of Gothenburg statistics seminar\n\
nLecture held in MVL14.\n\nAbstract\nTopological Data Analysis has in the
past year attracted more attention in various fields such as in material s
ciences to study the properties of porous material or in statistics to stu
dy the asymptotic properties of random objects. However\, topological data
analysis still appears hard to grasp for many statisticians. \n\nThis tal
k intends to be an introduction to topological data analysis and therefore
does not require any background in the field. We will present an overview
of the different approaches in topological data analysis and will focus o
n the persistent homology approach. \nWe will present the framework of thi
s approach and its main mathematical objects. \nFinally\, we come back to
the land of Probability and will present a central limit theorem for the s
o-called Betti numbers obtained from stationary point processes\, non-nece
ssarily Poisson.\n
LOCATION:https://researchseminars.org/talk/gbgstats/1/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Anders Ståhlberg & Serik Sagitov (Chalmers & University of Gothen
burg)
DTSTART;VALUE=DATE-TIME:20221006T131500Z
DTEND;VALUE=DATE-TIME:20221006T140000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/2
DESCRIPTION:Title: Counting molecular identifiers in sequencing using a multitype branching
process with immigration\nby Anders Ståhlberg & Serik Sagitov (Chalme
rs & University of Gothenburg) as part of Gothenburg statistics seminar\n\
nLecture held in MVL14.\n\nAbstract\nDetection of extremely rare variant a
lleles\, such as tumour DNA\, within a complex mixture of DNA molecules is
experimentally challenging due to sequencing errors. Barcoding of target
DNA molecules in library construction for next-generation sequencing provi
des a way to identify and bioinformatically remove polymerase induced erro
rs. During the barcoding procedure involving $t$ consecutive PCR cycles\,
the DNA molecules become barcoded by unique molecular identifiers (UMI). D
ifferent library construction protocols utilise different values of $t$. T
he effect of a larger $t$ and imperfect PCR amplifications is poorly descr
ibed. \n\nThis paper proposes a branching process with growing immigration
as a model describing the random outcome of $t$ cycles of PCR barcoding
. Our model discriminates between five different amplification rates $r_1$
\, $r_2$\, $r_3$\, $r_4$\, $r$ for different types of molecules associated
with the PCR barcoding procedure. We study this model by focussing on $C_
t$\, the number of clusters of molecules sharing the same \nUMI\, as well
as $C_t(m)$\, the number of UMI clusters of size $m$. Our main finding i
s a remarkable asymptotic pattern valid for moderately large $t$. It turns
out that \n$E(C_t(m))/E(C_t)\\approx 2^{-m}$ for $m=1\,2\,\\ldots$\, rega
rdless of the underlying parameters $(r_1\,r_2\,r_3\,r_4\,r)$. The knowled
ge of the quantities $C_t$ and $C_t(m)$ as functions of the experimental p
arameters $t$ and $(r_1\,r_2\,r_3\,r_4\,r)$ will help the users to draw mo
re adequate conclusions from the outcomes of different sequencing protocol
s.\n
LOCATION:https://researchseminars.org/talk/gbgstats/2/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Peter Guttorp (University of Washington/Norwegian computing center
)
DTSTART;VALUE=DATE-TIME:20221027T131500Z
DTEND;VALUE=DATE-TIME:20221027T140000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/3
DESCRIPTION:Title: Comparing recent climate models to data\nby Peter Guttorp (University
of Washington/Norwegian computing center) as part of Gothenburg statistic
s seminar\n\nLecture held in MVL14.\n\nAbstract\nThe latest climate model
intercomparison project (CMIP6) was the basis for the sixth assessment rep
ort of the Intergovernmental Panel on Climate Change. The design of CMIP6
included climate runs with historical forcings\, meant to be comparable to
observational data. We will focus on global annual mean temperature\, a c
ommon (but not particularly sensitive) measure of climate change. Using fo
ur observational products provided with uncertainty assessments\, we combi
ne these into a single series. In doing so\, we estimate a smooth trend an
d a residual spectral density function\, with attendant simultaneous confi
dence bands. Using the same kind of decomposition of 318 climate model run
s from 58 models in the historical CMIP6 experiment\, we see how well the
model runs agree with the data. We also compare the warming between 1880-1
899 and 1995-2014. This is joint work with Peter Craigmile of the Ohio Sta
te University.\n
LOCATION:https://researchseminars.org/talk/gbgstats/3/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Olle Häggström (Chalmers University of Technology & University o
f Gothenburg)
DTSTART;VALUE=DATE-TIME:20221013T131500Z
DTEND;VALUE=DATE-TIME:20221013T140000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/4
DESCRIPTION:Title: Anthropic reasoning and the hinge of history hypothesis\nby Olle Häg
gström (Chalmers University of Technology & University of Gothenburg) as
part of Gothenburg statistics seminar\n\nLecture held in MVL14.\n\nAbstrac
t\nAmong researchers and scholars working on existential risk and the long
-term future of humanity\, it has become increasingly common to speak of o
ur present time as uniquely pivotal for the long-term future - a notion th
at has become known under the term Hinge of History (HoH). Recently\, atte
mpts have been made to formalize this concept and work out whether we real
ly do live during the HoH. This involves not only a Bayesian analysis but
also controversial ideas in anthropic reasoning. I will review and critiqu
e this work and arrive at a nuanced answer to the HoH question and its pra
ctical ramifications.\n
LOCATION:https://researchseminars.org/talk/gbgstats/4/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Moritz Schauer (Chalmers University of Technology & University of
Gothenburg)
DTSTART;VALUE=DATE-TIME:20221020T131500Z
DTEND;VALUE=DATE-TIME:20221020T140000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/5
DESCRIPTION:Title: Automatic differentiation of programs with discrete randomness\nby Mo
ritz Schauer (Chalmers University of Technology & University of Gothenburg
) as part of Gothenburg statistics seminar\n\nLecture held in MVL14.\n\nAb
stract\nAutomatic differentiation (AD)\, a technique for constructing new
programs which compute the derivative of an original program\, has become
ubiquitous throughout scientific computing and deep learning due to the im
proved performance afforded by gradient-based optimization. However\, AD s
ystems have been restricted to the subset of programs that have a continuo
us dependence on parameters. Programs that have discrete stochastic behavi
ors governed by distribution parameters\, such as flipping a coin with pro
bability p of being heads\, pose a challenge to these systems because the
connection between the result (heads vs tails) and the parameters (p) is f
undamentally discrete. In this paper we develop a new reparameterization-b
ased methodology that allows for generating programs whose expectation is
the derivative of the expectation of the original program. We showcase how
this method gives an unbiased and low-variance estimator which is as auto
mated as traditional AD mechanisms. We demonstrate unbiased forward-mode A
D of discrete-time Markov chains\, agent-based models such as Conway's Gam
e of Life\, and unbiased reverse-mode AD of a particle filter. Our code is
available at https://github.com/gaurav-arya/StochasticAD.jl\n
LOCATION:https://researchseminars.org/talk/gbgstats/5/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Johan Jonasson (Chalmers University and University of Gothenburg)
DTSTART;VALUE=DATE-TIME:20230119T141600Z
DTEND;VALUE=DATE-TIME:20230119T150000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/6
DESCRIPTION:Title: Noise sensitivity/stability for deep Boolean neural nets\nby Johan Jo
nasson (Chalmers University and University of Gothenburg) as part of Gothe
nburg statistics seminar\n\nLecture held in MVL14.\n\nAbstract\nA well-kno
wn and ubiquitous property of neural net classifiers is that they can be f
ooled into misclassifying some objects by changing the input in tiny ways
that are indistinguishable for the human eye. These changes can be adversa
rial\, but sometimes they can be just random noise. This makes it interest
ing to ask if this property is something that almost all neural nets have
and\, when they do\, why that is. There are good heuristic explanations\,
but to prove mathematically rigorous results seems very difficult in gener
al. Here we prove some first results on various toy models. We treat our q
uestions within the framework of the established field of noise sensitivit
y/stability. What we prove can roughly be stated as:\n \n- \nA suffi
ciently deep fully connected network with sufficiently wide layers and iid
Gaussian weights is noise sensitive\, i.e. an arbitrarily small random no
ise makes the predicted class of a binary input string before and after th
e noise is added virtually independent. If one imposes correlations on the
weights corresponding to the same input features\, this still holds unles
s the correlation is very close to 1.
\n- \nNeural nets consisting o
f only convolutional layers may or may not be noise sensitive and we prese
nt examples of both behaviours.
\n

\n
LOCATION:https://researchseminars.org/talk/gbgstats/6/
END:VEVENT
BEGIN:VEVENT
SUMMARY:David Widmann (Uppsala University)
DTSTART;VALUE=DATE-TIME:20221124T141500Z
DTEND;VALUE=DATE-TIME:20221124T150000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/7
DESCRIPTION:Title: Calibration of probabilistic predictive models\nby David Widmann (Upp
sala University) as part of Gothenburg statistics seminar\n\nLecture held
in MVL14.\n\nAbstract\nMost supervised machine learning tasks are subject
to irreducible prediction errors. Probabilistic predictive models address
this limitation by providing probability distributions that represent a be
lief over plausible targets\, rather than point estimates. Such models can
be a valuable tool in decision-making under uncertainty\, provided that t
he model output is meaningful and interpretable. Calibrated models guarant
ee that the probabilistic predictions are neither over- nor under-confiden
t. In the machine learning literature\, different measures and statistical
tests have been proposed and studied for evaluating the calibration of cl
assification models. For regression problems\, however\, research has been
focused on a weaker condition of calibration based on predicted quantiles
for real-valued targets. In this paper\, we propose the first framework t
hat unifies calibration evaluation and tests for general probabilistic pre
dictive models. It applies to any such model\, including classification an
d regression models of arbitrary dimension. Furthermore\, the framework ge
neralizes existing measures and provides a more intuitive reformulation of
a recently proposed framework for calibration in multi-class classificati
on. In particular\, we reformulate and generalize the kernel calibration e
rror\, its estimators\, and hypothesis tests using scalar-valued kernels\,
and evaluate the calibration of real-valued regression problems.\n
LOCATION:https://researchseminars.org/talk/gbgstats/7/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Karin Hårding and Daire Carroll (University of Gothenburg)
DTSTART;VALUE=DATE-TIME:20221208T141500Z
DTEND;VALUE=DATE-TIME:20221208T150000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/8
DESCRIPTION:Title: Population dynamics and ecology of seal populations\, empirical data and
the search for theory to help our understanding. Stochastic growth models\
, image analysis\, spatial distribution and telemetry data on migrations\nby Karin Hårding and Daire Carroll (University of Gothenburg) as part
of Gothenburg statistics seminar\n\nLecture held in MVL14.\n\nAbstract\nT
his talk is about how statistical and mathematical methods are helpful whe
n we try to understand processes in wildlife populations. The European har
bour seal (Sw: knubbsälen) has been studied carefully for 40 years and th
e long time series allows analysis of how population growth is regulated.
Recently the population growth has declined and we visited the colonies t
o try to document in detail what is going on in order to give better advis
e to managers. We develop new methods for estimating body size from drones
and for counting seals from photos with machine learning algorithms. We a
pply stochastic population growth models\, dynamic energy budget models\,
and we discuss what is density dependence in age structured populations in
a variable environment. We are also interested in new collaborations and
feed back and look forward to interesting discussions on ways forward. Wel
come! Karin and Daire\n\nKarin Harding is professor in animal ecology with
a focus on marine mammals at GU and Daire Carroll is postdoctoral researc
her and develops new digital tools for wildlife ecology.\n
LOCATION:https://researchseminars.org/talk/gbgstats/8/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Khalil Shafie Holighi (University of Northern Colorado)
DTSTART;VALUE=DATE-TIME:20221201T141500Z
DTEND;VALUE=DATE-TIME:20221201T150000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/9
DESCRIPTION:Title: A test for multiple signal detection from noisy images\nby Khalil Sha
fie Holighi (University of Northern Colorado) as part of Gothenburg statis
tics seminar\n\nLecture held in MVL14.\n\nAbstract\nGaussian random field
theory has been extensively used to model the brain images.\nIn this work
\, I use the reproducing kernel Hilbert space (RKHS) machinery to derive
the likelihood ratio test statistic for activation signal detection in
functional magnetic resonance imaging. The models considered have the f
orm of smoothed version of signal plus a white noise which include sc
ale and rotation space random fields with one or more signals as special
cases.\n\nKhalil Shafie Holighi is professor of statistics at University
of Northern Colorado.\n
LOCATION:https://researchseminars.org/talk/gbgstats/9/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Peter Guttorp (University of Washington/Norwegian computing center
)
DTSTART;VALUE=DATE-TIME:20221215T141500Z
DTEND;VALUE=DATE-TIME:20221215T150000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/10
DESCRIPTION:Title: Vadan och varthän?\nby Peter Guttorp (University of Washington/Norw
egian computing center) as part of Gothenburg statistics seminar\n\nLectur
e held in MVL14.\n\nAbstract\nSverige har haft professurer is statistik (o
ch statskunskap) sedan början av 1900-talet. En av dessa var Pontus Fahlb
eck i Lund\, som förespråkade enbart en samhällsvetenskaplig inriktning
på statistiken. Lundaastronomen Carl Charlier sysslade med stellar stati
stik\, och ansåg att statistikvetenskapen var användbar i naturvetenskap
lika väl som samhällskunskap. Vi berättar hur denna konflikt resultera
de i institutioner både i statistik och matematisk statistik. Vi föresl
år att statistikvetenskapen\, vare sig den kallas statistik eller matemat
isk statistik\, bör hamna i en egen institution.\n
LOCATION:https://researchseminars.org/talk/gbgstats/10/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Alan Gelfand (Department of Statistical Science\, Duke University)
DTSTART;VALUE=DATE-TIME:20230316T121500Z
DTEND;VALUE=DATE-TIME:20230316T130000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/11
DESCRIPTION:Title: Three Spatial Data Fusion Vignettes\nby Alan Gelfand (Department of
Statistical Science\, Duke University) as part of Gothenburg statistics se
minar\n\nLecture held in MVL14.\n\nAbstract\nWith increased collection of
spatial (and spatio-temporal) datasets\, we often find multiple sources th
at are capable of informing about features of a process of interest. Throu
gh suitable fusion of the data sources\, we can learn at least as much abo
ut the process features of interest than from any individual source. For
three different illustrative ecological/environmental applications\, this
talk will propose suitable coherent stochastic modeling to implement a fus
ion of these sources. We focus exclusively on approaches that arise throug
h generative hierarchical modeling\; the specification could produce the d
ata sources that have been observed. Such modeling enables full inference
both with regard to estimation and prediction\, with implicit incorporati
on of uncertainty. \n\nWe consider the general setting of points and mark
s\, modeled as $[points][marks|points]$\, points in $\\mathcal{D}$\, marks
in $\\mathcal{Y}$. The process can model the points themselves\, the mar
ks themselves (ignoring any randomness in the points)\, or the points and
marks jointly. This results in four data types: (i) a point pattern\, $\\
mathcal{S}= (\\textbf{s}_{1}\, \\textbf{s}_{2}\,\\ldots\,\\textbf{s}_{n})$
\, (ii) a vector of counts for sets\, $\\{N(B_{k})\, k=1\,2\,\\ldots\,K\\}
$\, (iii) a vector of observations at points\, $\\{Y(\\textbf{s}_{i})\,i=1
\,2\,\\ldots\,n\\}$\, (iv) a vector of averages for sets\, $\\{Y(B_{1})\,
Y(B_{2})\,\\ldots\,Y(B_{k})\\}$. We illustrate with two data sources\; ea
ch can be any one of the four data types. Regardless of how the data are
observed\, we imagine the process operates at point level. Further\, we im
agine a stochastic process over $\\mathcal{D}$ which links the two data so
urces.\n\nThe first vignette considers presence/absence data over $\\mathc
al{D}$ with one dataset being presence/absence of a species collected at a
set of chosen locations. The other data source is in the form of museum/
citizen science data\, recording random locations where the species was ob
served. The goal is to better understand the probability of presence surf
ace over $\\mathcal{D}$. The second vignette considers zooplankton abundan
ce data gathered through two different $\\it{towing}$ mechanisms. One mec
hanism is calibrated while the other is not. The goal is to better unders
tand zooplankton abundance over $\\mathcal{D}$. The third\, and most chal
lenging vignette seeks to learn about whale abundance. Here\, the two sou
rces are aerial distance sampling data for whale sightings and passive aco
ustic monitoring data (using monitors on the ocean floor) for whale calls.
\n\nThis is joint work with Shin Shirota\, Jorge Castillo-Mateo\, Erin Sc
hliep\, and Rob Schick.\n
LOCATION:https://researchseminars.org/talk/gbgstats/11/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Martin Voigt Vejling (Aalborg University)
DTSTART;VALUE=DATE-TIME:20230216T121500Z
DTEND;VALUE=DATE-TIME:20230216T130000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/12
DESCRIPTION:Title: Applications of point process models to wireless communication systems\nby Martin Voigt Vejling (Aalborg University) as part of Gothenburg sta
tistics seminar\n\nLecture held in MVL15.\n\nAbstract\nWireless communicat
ions is a field in engineering that is attracting a lot of attention and o
ne of the envisioned enablers of future communication systems is statistic
al learning. Interestingly\, many scenarios considered in wireless communi
cations is naturally modelled by point processes. This has historically be
en studied with stochastic geometry models for wireless networks\, however
\, some areas of applications remain underexplored.\n\nIn this talk\, I wi
ll introduce the audience to what wireless communication is\, discussing t
he basic concepts\, models\, and challenges. This includes discussing the
typically used parametric channel model and how harmonic analysis\, estima
tion theory\, and compressive sensing are central mathematical topics appl
ied in practice. Then\, I will motivate the use of point process models wi
thin wireless communications by giving a general modelling perspective. Fi
nally\, I will discuss the primary focus of my research in radio frequency
sensing powered by statistical learning for point processes.\n\nThe speak
er is a PhD student shared between the Department of Electronic Systems an
d the Department of Mathematical Sciences at Aalborg University. The talk
is intended for researchers in spatial statistics interested in applicatio
ns to topics in wireless communications.\n
LOCATION:https://researchseminars.org/talk/gbgstats/12/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Torgny Lindvall (Chalmers University of Technology & University of
Gothenburg)
DTSTART;VALUE=DATE-TIME:20230202T121500Z
DTEND;VALUE=DATE-TIME:20230202T130000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/13
DESCRIPTION:Title: On coupling of renewal processes and random walks\nby Torgny Lindval
l (Chalmers University of Technology & University of Gothenburg) as part o
f Gothenburg statistics seminar\n\nLecture held in MVL15.\n\nAbstract\nWe
use an Ornstein coupling for another proof of Blackwell's renewal theorem\
, and a Mineka coupling to establish a 0-2 law for random walks.\n
LOCATION:https://researchseminars.org/talk/gbgstats/13/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Mike Pereira (Mines Paris - PSL University)
DTSTART;VALUE=DATE-TIME:20230223T121500Z
DTEND;VALUE=DATE-TIME:20230223T130000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/15
DESCRIPTION:Title: Gaussian fields on Riemannian manifolds: Application to Geostatistics.\nby Mike Pereira (Mines Paris - PSL University) as part of Gothenburg s
tatistics seminar\n\nLecture held in MVL15.\n\nAbstract\nMany applications
in spatial statistics require data to be modeled by Gaussian processes on
non-Euclidean domains\, or with non-stationary properties. Using such mo
dels generally comes at the price of a drastic increase in operational cos
ts (computational and storage-wise)\, rendering them hard to apply to larg
e datasets. In this talk\, we propose a solution to this problem\, which r
elies on the definition of a class of random fields on Riemannian manifold
s. These fields extend ongoing work that has been done to leverage a chara
cterization of the random fields classically used in Geostatistics as solu
tions of stochastic partial differential equations. The discretization of
these generalized random fields\, undertaken using a finite element approa
ch\, then provides an explicit characterization that is leveraged to solve
the scalability problem. Indeed\, matrix-free algorithms\, in the sense t
hat they do not require to build and store any covariance (or precision) m
atrix\, are derived to tackle for instance the simulation of large Gaussia
n fields with given covariance properties\, even in the non-stationary set
ting.\n
LOCATION:https://researchseminars.org/talk/gbgstats/15/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Konstantinos Konstantinou (Chalmers University of Technology & Uni
versity of Gothenburg)
DTSTART;VALUE=DATE-TIME:20230309T121500Z
DTEND;VALUE=DATE-TIME:20230309T130000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/16
DESCRIPTION:Title: Global tests for quantile regression with applications in modeling distr
ibutions.\nby Konstantinos Konstantinou (Chalmers University of Techno
logy & University of Gothenburg) as part of Gothenburg statistics seminar\
n\nLecture held in MVL15.\n\nAbstract\nIn this talk\, I will give an intro
duction to global tests for quantile regression\, i.e.\, statistical tests
allowing for simultaneous inference of the quantile regression process\,
with graphical interpretation. The proposed global quantile regression tes
ts can determine not only if there is a difference\, but it can also deter
mine for which quantiles the difference is significant on the global signi
ficance level. The case where the effect of a factor (e.g.\, a categorical
factor giving the group) on the distribution functions is of interest but
confounded with other factors affecting the distributions is studied. An
extensive simulation study is conducted to compare the global quantile reg
ression tests with classical graphical tests based on the Kolmogorov-Smirn
ov test statistic. This is a joint work with Tomáš Mrkvička\, Mari Myll
ymäki and Mikko Kuronen.\n
LOCATION:https://researchseminars.org/talk/gbgstats/16/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Finn Lindgren (University of Edinburgh)
DTSTART;VALUE=DATE-TIME:20230314T121500Z
DTEND;VALUE=DATE-TIME:20230314T130000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/17
DESCRIPTION:Title: Stochastic adventures in space and time\nby Finn Lindgren (Universit
y of Edinburgh) as part of Gothenburg statistics seminar\n\nLecture held i
n MVL14.\n\nAbstract\nThe standard geostatistics toolbox includes methods
for modelling\nspatial dependence between georeferenced observations\, as
well as\nmethods for modelling the occurrence of random points. The core\
nmodel building blocks are often some form of Gaussian random fields.\n\nT
he easiest approach to constructing space-time models is by taking\nthe pr
oduct between a spatial covariance kernel and a temporal\ncovariance kerne
l. These are called covariance separable models. An\nalternative that may
better capture the spatio-temporal dynamics is to\ntake inspiration for ph
ysics motivated partial differential equations\nsuch as the heat equation\
, which leads to non-separable models.\nNon-separable models are in genera
l more computationally expensive\,\nbut one can sometimes use the model st
ructure to retain a lot of the\nsimplicity of separable models\, for examp
le allowing these models to\nbe used as components of larger hierarchical
generalised additive\nmodels. For point process observations\, such as obs
ervations of a\nmoving animal\, the temporal dynamics poses an additional
challenge.\n\nI will discuss some of these aspects\, including a basic con
struction\nof non-separable space-time models\, as well as an application
of the\nINLA/inlabru framework to estimate the parameters of a dynamical\n
animal movement model by rephrasing it as a point process model\, with\na
parametric movement kernel\, and a random field as an unknown\n"resource s
election function".\n
LOCATION:https://researchseminars.org/talk/gbgstats/17/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Nicoletta D’Angelo (University of Palermo)
DTSTART;VALUE=DATE-TIME:20230404T111500Z
DTEND;VALUE=DATE-TIME:20230404T120000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/18
DESCRIPTION:Title: Self-exciting point process modelling of crimes on linear networks\n
by Nicoletta D’Angelo (University of Palermo) as part of Gothenburg stat
istics seminar\n\nLecture held in MVL15.\n\nAbstract\nAlthough there are r
ecent developments in analysing first and second-order characteristics of
point processes on networks\, there are very few attempts to introduce mod
els for network data.\nMotivated by the analysis of crime data in Bucarama
nga (Colombia)\, we propose a spatio-temporal Hawkes point process model a
dapted to events living on linear networks. We first consider a non-param
etric modelling strategy\, for which we follow a non-parametric estimation
of both the background and the triggering components. Then we consider a
semi-parametric version\, including a parametric estimation of the backgro
und based on covariates. Our network model outperforms a planar version\,
improving the fitting of the self-exciting point process model\, and can b
e easily adapted to multi-type processes.\n
LOCATION:https://researchseminars.org/talk/gbgstats/18/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Petar Jovanovski (Chalmers University of Technology & University o
f Gothenburg)
DTSTART;VALUE=DATE-TIME:20230420T111500Z
DTEND;VALUE=DATE-TIME:20230420T120000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/23
DESCRIPTION:Title: Approximate Bayesian Computation with Backward Simulation for Discretely
Observed Diffusions\nby Petar Jovanovski (Chalmers University of Tech
nology & University of Gothenburg) as part of Gothenburg statistics semina
r\n\nLecture held in MVL14.\n\nAbstract\nStochastic differential equations
(SDE) are employed in many areas of science as a powerful tool for modell
ing processes that are subject to random fluctuations. Bayesian inference
for a large class of SDEs is challenging due to the analytic intractabilit
y of the likelihood function. Nevertheless\, forward simulation via numeri
cal methods is straightforward\, motivating the use of approximate Bayesia
n computation (ABC). We propose a simulation scheme for SDE models that is
based on processing the observation in both the forward and backward dire
ction\, effectively utilizing the information provided by the observed dat
a. This leads to the simulation of sample paths that are consistent with t
he observations\, thereby increasing the ABC acceptance rate. We additiona
lly leverage partial exchangeability of Markov processes and employ invari
ant neural networks to learn the summary statistics that are needed in ABC
. These are sequentially learned by exploiting a sequential Monte Carlo AB
C sampler\, which provides new training data at each iteration. Therefore\
, our novel contribution is a learning tool for SDE model parameters while
simultaneously learning the summary statistics. Using synthetic data gene
rated from the Chan-Karaolyi-Longstaff-Sanders SDE family\, we show that o
ur approach accelerates inference considerably\, compared to standard (for
ward-only) methods\, while preserving inference accuracy.\n
LOCATION:https://researchseminars.org/talk/gbgstats/23/
END:VEVENT
BEGIN:VEVENT
SUMMARY:David Bolin (King Abdullah University of Science and Technology)
DTSTART;VALUE=DATE-TIME:20230427T111500Z
DTEND;VALUE=DATE-TIME:20230427T120000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/24
DESCRIPTION:Title: Gaussian Whittle-Matérn fields on metric graphs\nby David Bolin (Ki
ng Abdullah University of Science and Technology) as part of Gothenburg st
atistics seminar\n\nLecture held in MVL14.\n\nAbstract\nWe define a new cl
ass of Gaussian processes on compact metric graphs such as street or river
networks. The proposed models\, the Whittle-Matérn fields\, are defined
via a fractional stochastic partial differential equation on the compact m
etric graph and are a natural extension of Gaussian fields with Matérn co
variance functions on Euclidean domains to the non-Euclidean metric graph
setting. Existence of the processes\, as well as their sample path regular
ity properties are derived. The model class in particular contains differe
ntiable Gaussian processes. To the best of our knowledge\, this is the fir
st construction of a valid differentiable Gaussian field on general compac
t metric graphs.\nWe then focus on a model subclass which we show contains
processes with Markov properties. For this case\, we show how to evaluate
finite dimensional distributions of the process exactly and computational
ly efficiently. This facilitates using the proposed models for statistical
inference without the need for any approximations. Finally\, we derive so
me of the main statistical properties of the model class\, such as consist
ency of maximum likelihood estimators of model parameters and asymptotic o
ptimality properties of linear prediction based on the model with misspeci
fied parameters. \nThe usage of the model class is illustrated through an
application to traffic data.\n
LOCATION:https://researchseminars.org/talk/gbgstats/24/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Moa Johansson (Chalmers University of Technology)
DTSTART;VALUE=DATE-TIME:20230504T111500Z
DTEND;VALUE=DATE-TIME:20230504T120000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/25
DESCRIPTION:Title: Machine Learning Methods for texts from Political Science\nby Moa Jo
hansson (Chalmers University of Technology) as part of Gothenburg statisti
cs seminar\n\nLecture held in MVL14.\n\nAbstract\nIn the WASP-HS project "
Bias and Methods of AI Technology Studying Political Behavior" we are inve
stigating and developing machine learning methods to help political scient
ists study the enormous amounts of text documents that are otherwise beyon
d manual analysis\, such as the document repository from the Swedish Riksd
ag. This is a collaborative project between Dr. Annika Fredén's group in
the Political Science department at Lund University\, and the group of Dr.
Moa Johansson at Computer Science at Chalmers.\n\nI will give an overview
of some of the work so far on how we are trying to highlight differences
in language use between parties in the Swedish Riksdag. The first paper is
about comparing word embeddings trained on texts from different parties.
The second concerns explainability of text classification: if a machine le
arning algorithm can classify text as belonging to one party or another\,
it is useful for a social scientist to know what such a classification is
based on. We have started to develop a new method for class explainability
for text for this purpose. This is going work with PhD student Denitsa Sa
ynova\, and post-doc Bastiaan Bruinsma.\n
LOCATION:https://researchseminars.org/talk/gbgstats/25/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Oskar Allerbo (Chalmers University of Technology & University of G
othenburg)
DTSTART;VALUE=DATE-TIME:20230511T111500Z
DTEND;VALUE=DATE-TIME:20230511T120000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/26
DESCRIPTION:Title: Solving Kernel Ridge Regression with Gradient Descent\nby Oskar Alle
rbo (Chalmers University of Technology & University of Gothenburg) as part
of Gothenburg statistics seminar\n\nLecture held in MVL14.\n\nAbstract\nW
e present an equivalent formulation for the objective function of kernel r
idge regression (KRR)\, that opens up for studying KRR from the perspectiv
e of gradient descent. Utilizing gradient descent with infinitesimal step
size\, allows us to formulate a new regularization for kernel regression
through early stopping.\n\nThe gradient descent formulation of KRR allo
ws us expand to a time dependent stationary kernel\, where we decrease the
bandwidth to zero during training. This circumvents the need of hyper par
ameter selection. Furthermore\, we are able to achieve both zero train
ing error and a double descent behavior\, phenomena that do not occur for
KRR with constant bandwidth\, but are known to appear for neural networks.
\n\nThe new formulation of KRR also enables us to explore other penalties
than the ridge penalty. Specifically\, we explore the $\\ell_1$ and $\\ell
_\\infty$ penalties and show that these correspond to two flavors of gradi
ent descent\, thus alleviating the need of computationally heavy proximal
gradient descent algorithms. We show theoretically and empirically how the
se formulations correspond to signal-driven and robust regression\, respec
tively.\n
LOCATION:https://researchseminars.org/talk/gbgstats/26/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Juan Inda (Chalmers University of Technology & University of Gothe
nburg)
DTSTART;VALUE=DATE-TIME:20230516T111500Z
DTEND;VALUE=DATE-TIME:20230516T120000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/27
DESCRIPTION:Title: Confidence-based Prediction of Antibiotic Resistance at the Patient-leve
l Using Transformers\nby Juan Inda (Chalmers University of Technology
& University of Gothenburg) as part of Gothenburg statistics seminar\n\nLe
cture held in MVL15.\n\nAbstract\nRapid and accurate diagnostics of bacter
ial infections are necessary for efficient treatment of antibiotic-resista
nt pathogens. Cultivation-based methods\, such as antibiotic susceptibilit
y testing (AST)\, are slow\, resource-demanding\, and can fail to produce
results before the treatment needs to start. This increases patient risks
and antibiotic overprescription. Here\, we present a deep-learning method
that uses transformers to merge patient data with available AST results to
predict antibiotic susceptibilities that have not been measured. The meth
od is combined with conformal prediction (CP) to enable the estimation of
uncertainty at the patient-level. After training on three million AST resu
lts from thirty European countries\, the method made accurate predictions
for most antibiotics while controlling the error rates\, even when limited
diagnostic information was available. We conclude that transformers and C
P enables confidence-based decision support for bacterial infections and\,
thereby\, offer new means to meet the growing burden of antibiotic resist
ance.\n
LOCATION:https://researchseminars.org/talk/gbgstats/27/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Frank Miller (Linköping University and Stockholm University)
DTSTART;VALUE=DATE-TIME:20230525T111500Z
DTEND;VALUE=DATE-TIME:20230525T120000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/28
DESCRIPTION:Title: Parallel optimal pretesting of mixed-format questions for achievement te
sts\nby Frank Miller (Linköping University and Stockholm University)
as part of Gothenburg statistics seminar\n\nLecture held in MVL14.\n\nAbst
ract\nWhen large achievement tests like national tests in school or admiss
ion tests for university are conducted regularly\, the test-questions need
to be pretested before being used in the test. Methods for assigning pret
esting questions to examinees in an optimal way based on their ability hav
e been developed. Most of these methods are intended for a situation where
examinees arrive sequentially for being assigned to pretesting questions.
However\, several pretests (e.g.\, for national tests in Swedish schools
or for högskoleprovet) are conducted in a way where all or many examinees
conduct the test in parallel. In this talk\, we develop an optimal design
for such parallel pretest setups which can be implemented in real scenari
os. In many real test situations\, questions are of mixed format and our o
ptimal design method can handle that. We discuss first the optimal designs
for the 2-parameter logistic\, the 3-parameter logistic\, and the general
ized partial credit model. Then\, we consider the case of mixed-format tes
ts where all these models are used to fit the data. The method we propose
can also take different expected solve times into consideration. We invest
igate the efficiency gain of the method. Our investigations show that the
proposed method is able to increase the efficiency of pretests considerabl
y. The described method has been used for the Swedish national tests in ma
thematics. \n\nThis is a joint work with Ellinor Fackle-Fornius\n
LOCATION:https://researchseminars.org/talk/gbgstats/28/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Lotta Eriksson (Chalmers University of Technology & University of
Gothenburg)
DTSTART;VALUE=DATE-TIME:20230601T111500Z
DTEND;VALUE=DATE-TIME:20230601T120000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/29
DESCRIPTION:Title: A multitype Galton-Watson model of biological aging\nby Lotta Erikss
on (Chalmers University of Technology & University of Gothenburg) as part
of Gothenburg statistics seminar\n\nLecture held in MVL14.\n\nAbstract\nTh
e progressive accumulation of damaged proteins within the cellular structu
re is a key factor in the aging process of yeast. By considering the quant
ity of damaged proteins as a measure of the cell's biological age\, we exp
lore an individual-based stochastic population model that incorporates rej
uvenation events. During cell division\, the mother cell is given the oppo
rtunity rejuvenate\, by transferring the accumulated damage to the daughte
r cell. This modeling approach allows us to study the dynamics of the agin
g process and understand the impact of rejuvenation on the overall populat
ion.\n
LOCATION:https://researchseminars.org/talk/gbgstats/29/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Felix Held (Chalmers University of Technology & University of Goth
enburg)
DTSTART;VALUE=DATE-TIME:20230608T111500Z
DTEND;VALUE=DATE-TIME:20230608T120000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/30
DESCRIPTION:Title: Simultaneous gene clustering and regulatory program reconstruction revea
ls insight into the phenotypic plasticity of neural cancers\nby Felix
Held (Chalmers University of Technology & University of Gothenburg) as par
t of Gothenburg statistics seminar\n\nLecture held in MVL14.\n\nAbstract\n
Nervous system cancers contain a large spectrum of transcriptional cell st
ates\, reflecting processes active during normal development\, injury resp
onse and growth. However\, we lack a good understanding of these states' r
egulation and pharmacological importance. Here\, we describe the integrate
d reconstruction of such cellular regulatory programs and their therapeuti
c targets from extensive collections of single-cell RNA sequencing data (s
cRNA-seq). Our approach called single-cell Regulatory-driven Clustering (s
cRegClust) performs simultaneous gene clustering and regulatory program re
construction tasks to predict essential kinases and transcription factors.
We formulate an apriori intractable partitioning problem that connects ge
ne modules with linear regulator models. A greedy two-step procedure is co
nstructed to iteratively update gene modules and associated regulatory pro
grams and find an approximate solution. Penalized regression was used to r
eplace a combinatorial selection problem in the construction of regulatory
programs and predictive modelling was used during gene cluster allocation
. The method is used to identify regulatory programs in tumor cell states
from both adult and childhood brain cancers. Further analysis corroborated
by experimental results leads to hypothesis generation on an underlying b
iological mechanism for drug combination therapy in adult glioblastoma.\n
LOCATION:https://researchseminars.org/talk/gbgstats/30/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Sara Hamis (Tampere University)
DTSTART;VALUE=DATE-TIME:20231026T111500Z
DTEND;VALUE=DATE-TIME:20231026T120000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/31
DESCRIPTION:Title: Spatial cumulant models for mathematical cancer research\nby Sara Ha
mis (Tampere University) as part of Gothenburg statistics seminar\n\nLectu
re held in MVL14.\n\nAbstract\nSpatial cumulant models (SCMs) are spatiall
y resolved population models\, formulated by differential equations\, that
describe population dynamics generated by spatio-temporal point processes
(STPPs). Specifically\, SCMs approximate the dynamics of two STPP-generat
ed summary statistics: first-order spatial cumulants (densities)\, and sec
ond-order spatial cumulants (spatial covariances). \n\nIn this talk\, I’
ll exemplify how SCMs can be used in mathematical oncology by modelling th
eoretical cancer cell populations comprising interacting subclones. Our re
sults demonstrate that SCMs can capture STPP-generated population density
dynamics\, even when mean-field population models (MFPMs) fail to do so. F
rom both MFPM and SCM equations\, we derive treatment-induced death rates
required to achieve non-growing cell populations. When testing these treat
ment strategies in STPP-generated cell populations\, our results demonstra
te that SCM-informed strategies outperform MFPM-informed strategies in ter
ms of inhibiting population growths. We thus demonstrate that SCMs provide
a new framework in which to study cell-cell interactions and treatments t
hat take cell-cell interactions into account. \n\nJoint work with: Panu So
mervuo\; J. Arvid Ågren\; Dagim S. Tadele\; Juha Kesseli\; Jacob G. Scott
\; Matti Nykter\; Philip Gerlee\; Dmitri Finkelshtein\; Otso Ovaskainen.\n
LOCATION:https://researchseminars.org/talk/gbgstats/31/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Hrvoje Planinić (University of Zagreb)
DTSTART;VALUE=DATE-TIME:20230921T091500Z
DTEND;VALUE=DATE-TIME:20230921T100000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/32
DESCRIPTION:Title: Extremes of stationary heavy-tailed time series\nby Hrvoje Planinić
(University of Zagreb) as part of Gothenburg statistics seminar\n\nLectur
e held in MVL14.\n\nAbstract\nWe will present a framework for describing t
he asymptotic behavior of high-level exceedances for stationary (i.e. depe
ndent) time series with heavy-tailed marginal distribution and whose excee
dances occur in clusters\; think of modelling e.g. financial returns or da
ily rainfall measurements. The main tools are the theory of point processe
s and the notion of the so-called tail process. The latter allows one to f
ully describe the asymptotic distribution of the extremal clusters using t
he language of standard Palm theory. We will illustrate the general theory
on simple moving average models. If time permits\, we will comment on how
this framework can be extended to deal with extremes related to models fr
om stochastic geometry.\n
LOCATION:https://researchseminars.org/talk/gbgstats/32/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Devdatt Dubhashi (Chalmers)
DTSTART;VALUE=DATE-TIME:20230928T111500Z
DTEND;VALUE=DATE-TIME:20230928T120000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/33
DESCRIPTION:Title: Bandits: Structured and Constrained\nby Devdatt Dubhashi (Chalmers)
as part of Gothenburg statistics seminar\n\nLecture held in MVL14.\n\nAbst
ract\nI will give an introduction and invitation to Bandits - a very simpl
e\, yet central model of sequential decision making under uncertainty. Aft
er introducing the central concepts and some of the basic algorithms and r
esults\, I'll describe some recent work from our group on some extensions
of the basic model which are also useful in applications. Throughout I'll
try to show how the subject has close connections to information theory\,
statistics and optimization.\n
LOCATION:https://researchseminars.org/talk/gbgstats/33/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Serik Sagitov (Chalmers University of Technology & university of G
othenburg)
DTSTART;VALUE=DATE-TIME:20231103T121500Z
DTEND;VALUE=DATE-TIME:20231103T130000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/35
DESCRIPTION:Title: Theta-positive branching processes in varying environment\nby Serik
Sagitov (Chalmers University of Technology & university of Gothenburg) as
part of Gothenburg statistics seminar\n\nLecture held in MVL14.\n\nAbstrac
t\nBranching processes in a varying environment encompass a wide range of
stochastic demographic models\, and their complete understanding in terms
of limit behavior poses a formidable research challenge. In this paper\, w
e conduct a thorough investigation of such processes within a continuous-t
ime framework\, assuming that the reproduction law of individuals adheres
to a specific parametric form for the probability generating function. Our
six clear-cut limit theorems support the notion of recognizing five disti
nct asymptotical regimes for branching in varying environments: supercriti
cal\, asymptotically degenerate\, critical\, strictly subcritical\, and lo
osely subcritical.\n
LOCATION:https://researchseminars.org/talk/gbgstats/35/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Olle Häggström (Chalmers University of Technology & university o
f Gothenburg)
DTSTART;VALUE=DATE-TIME:20231006T111500Z
DTEND;VALUE=DATE-TIME:20231006T120000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/36
DESCRIPTION:Title: Playing with fire\nby Olle Häggström (Chalmers University of Techn
ology & university of Gothenburg) as part of Gothenburg statistics seminar
\n\nLecture held in MVL14.\n\nAbstract\nAlan Turing speculated in 1951 abo
ut a time point in the future when machines “outstrip our feeble powers
” in such a way that we lose our position as the most intelligent specie
s on the planet. Current AI trends suggest that we are rapidly approaching
that time point. This is playing with fire\, because at such a time point
our continued wellbeing hinges largely on what the first superintelligent
machines are motivated to do. If their goals and values are aligned with
ours\, then a brilliant future awaits us\, while if not\, then most likely
it is (in the words of OpenAI’s CEO Sam Altman) “lights out for every
one”. Making this transition go well involves considerable technological
and societal challenges.\n
LOCATION:https://researchseminars.org/talk/gbgstats/36/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Moritz Schauer (Chalmers University of Technology & University of
Gothenburg)
DTSTART;VALUE=DATE-TIME:20231020T090000Z
DTEND;VALUE=DATE-TIME:20231020T094500Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/37
DESCRIPTION:Title: Causal structure learning and sampling using Markov Monte Carlo with mom
entum\nby Moritz Schauer (Chalmers University of Technology & Universi
ty of Gothenburg) as part of Gothenburg statistics seminar\n\n\nAbstract\n
In the context of inferring a Bayesian network structure from observationa
l data\, that is inferring a directed acyclic graph (DAG)\, we devise a no
n-reversible continuous-time Markov chain that targets a probability distr
ibution over classes of observationally equivalent (Markov equivalent) DAG
s. The classes are represented as completed partially directed acyclic gra
phs (CPDAGs). The non-reversible Markov chain relies on the operators used
in Chickering’s Greedy Equivalence Search (GES) and is endowed with a m
omentum variable\, which improves mixing significantly as we show empirica
lly. The possible target distributions include posterior distributions bas
ed on a prior and a Markov equivalent likelihood. Joint work with Marcel W
ienöbst (Universität zu Lübeck).\n\nThis is a talk in the webinar serie
s of the Cramér society\n
LOCATION:https://researchseminars.org/talk/gbgstats/37/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Frédéric Lavancier (Nantes University\, France)
DTSTART;VALUE=DATE-TIME:20240119T121500Z
DTEND;VALUE=DATE-TIME:20240119T130000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/38
DESCRIPTION:Title: Spatial birth-death-move processes: basic properties and inference\n
by Frédéric Lavancier (Nantes University\, France) as part of Gothenburg
statistics seminar\n\nLecture held in MVL14.\n\nAbstract\nBirth-death-mov
e processes are Markov models for the spatio-temporal dynamics of a system
of particles in motion where births and deaths can occur. Natural applica
tions include epidemiology\, individual-based modelling in ecology and spa
tio-temporal dynamics observed in bio-imaging. We present some of the basi
c probabilistic properties of these processes and we consider two inferenc
e problems: First\, the non-parametric estimation of the birth and death i
ntensity functions\; Second\, the parametric estimation of the full dynami
cs by maximum likelihood. We finally apply our statistical method to the a
nalysis of a real dataset representing the spatio-temporel dynamics of bio
molecules observed in a living cell.\n
LOCATION:https://researchseminars.org/talk/gbgstats/38/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Christian Hirsch (Aarhus University)
DTSTART;VALUE=DATE-TIME:20240301T121500Z
DTEND;VALUE=DATE-TIME:20240301T130000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/41
DESCRIPTION:Title: On the topology of higher-order age-dependent random connection models\nby Christian Hirsch (Aarhus University) as part of Gothenburg statisti
cs seminar\n\nLecture held in MVL14.\n\nAbstract\nPreferential attachment
is a popular mechanism for generating scale-free networks. While it offers
a compelling narrative\, the underlying reinforced processes make it diff
icult to rigorously establish subtle properties. Recently\, age-dependent
random connection models were proposed as an alternative that is capable o
f generating similar networks with a mechanism that is amenable to a more
refined analysis. In this talk\, we analyze the asymptotic behavior of hig
her-order topological characteristics such as higher-order degree distribu
tions and Betti numbers in large domains. We demonstrate the practical ap
plication of the theoretical results to real-world datasets by analyzing s
cientific collaboration networks based on data from arXiv.This talk is bas
ed on joint work with Péter Juhász\n
LOCATION:https://researchseminars.org/talk/gbgstats/41/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Pierre Nyquist (Chalmers University of Technology & University of
Gothenburg)
DTSTART;VALUE=DATE-TIME:20240221T121500Z
DTEND;VALUE=DATE-TIME:20240221T130000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/42
DESCRIPTION:Title: Large deviations for Markov chain Monte Carlo methods: the surprisingly
curious case of Metropolis-Hastings.\nby Pierre Nyquist (Chalmers Univ
ersity of Technology & University of Gothenburg) as part of Gothenburg sta
tistics seminar\n\nLecture held in MVL14.\n\nAbstract\nMarkov chain Monte
Carlo (MCMC) methods have become the workhorse for numerical computations
in a range of scientific disciplines\, e.g.\, computational chemistry and
physics\, statistics\, and machine learning. The performance of MCMC metho
ds has therefore become an important topic at the intersection of probabil
ity theory and (computational) statistics: e.g.\, when the underlying dist
ribution one is trying to sample from becomes sufficiently complex\, conve
rgence speed and/or the cost per iteration becomes an issue for most MCMC
methods. \n\nThe analysis\, and subsequently design\, of MCMC methods has
to a large degree relied on classical tools used to determine the speed of
convergence of Markov chains\, e.g.\, mixing times\, spectral gap and fun
ctional inequalities (Poincaré\, log-Sobolev). An alternative avenue is t
o use the theory of large deviations for empirical measures. In this talk
I will first give a general outline of this approach to analysing MCMC met
hods\, along with some recent examples. I will then consider the specific
case of the Metropolis-Hastings algorithm\, the most classical amongst all
MCMC methods and a foundational building block for many more advanced met
hods. Despite the simplicity of this method\, it turns out that the theore
tical analysis of it is still a rich area\, and from the large deviation p
erspective it is surprisingly difficult to treat. As a first step we show
a large deviation principle for the underlying Markov chain\, extending th
e celebrated Donsker-Varadhan theory. Time permitted I will also discuss o
ngoing and future work on using this result for better understanding of bo
th the Metropolis-Hastings method and more advanced methods\, such as appr
oximate Bayesian computation (ABC-MCMC) and the Metropolis-adjusted Langev
in algorithm (MALA).\n\nThe talk will be self-contained and no prior knowl
edge of either MCMC methods or large deviations is required.\n\nThe talk i
s primarily based on join work with Federica Milinanni (KTH).\n
LOCATION:https://researchseminars.org/talk/gbgstats/42/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Vincent Szolnoky (Chalmers University of Technology & University o
f Gothenburg)
DTSTART;VALUE=DATE-TIME:20240306T121500Z
DTEND;VALUE=DATE-TIME:20240306T130000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/45
DESCRIPTION:Title: On the Interpretability of Regularisation for Neural Networks Through Mo
del Gradient Similarity\nby Vincent Szolnoky (Chalmers University of T
echnology & University of Gothenburg) as part of Gothenburg statistics sem
inar\n\nLecture held in MVL14.\n\nAbstract\nMost complex machine learning
and modelling techniques are prone to over-fitting and may subsequently ge
neralise poorly to future data. Artificial neural networks are no differen
t in this regard and\, despite having a level of implicit regularisation w
hen trained with gradient descent\, often require the aid of explicit regu
larisers. We introduce a new framework\, Model Gradient Similarity (MGS)\,
that (1) serves as a metric of regularisation\, which can be used to moni
tor neural network training\, (2) adds insight into how explicit regularis
ers\, while derived from widely different principles\, operate via the sam
e mechanism underneath by increasing MGS\, and (3) provides the basis for
a new regularisation scheme which exhibits excellent performance\, especia
lly in challenging settings such as high levels of label noise or limited
sample sizes.\n
LOCATION:https://researchseminars.org/talk/gbgstats/45/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Selma Tabakovic (Chalmers University of Technology & University of
Gothenburg)
DTSTART;VALUE=DATE-TIME:20240313T121500Z
DTEND;VALUE=DATE-TIME:20240313T130000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/46
DESCRIPTION:Title: AI-driven sepsis care: early detection and personalized treatment\nb
y Selma Tabakovic (Chalmers University of Technology & University of Gothe
nburg) as part of Gothenburg statistics seminar\n\nLecture held in MVL14.\
n\nAbstract\nSepsis is a life-threatening organ dysfunction caused by a dy
sregulated host response to infection\, and remains a leading cause of dea
th in intensive care units worldwide. An optimal treatment strategy is sti
ll unknown\, leading to a significant variability in sepsis treatment with
poorer outcomes.\n\nRecently\, deep reinforcement learning has shown prom
ise as a decision-aiding tool for the administration of intravenous fluids
and vasopressors to septic patients. However\, these models are limited i
n their ability to accommodate the entire range from high-risk to low-risk
patients\, and thus fail to provide personalized treatment recommendation
s.\n\nTo address this limitation\, in particular in the presence of hetero
geneous patient groups or heterogeneous treatment responses\, we propose a
Multi-Head Dueling Double Deep Q-Network (MH-DQN) model that incorporates
patient characteristics to enable more personalized treatment recommendat
ions. The MH-DQN model has multiple output layers\, each of which is optim
ized for a specific patient profile. The model is trained using the Medica
l Information Mart for Intensive Care (MIMIC-III) database.\n
LOCATION:https://researchseminars.org/talk/gbgstats/46/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Akash Sharma (Chalmers University of Technology & University of Go
thenburg)
DTSTART;VALUE=DATE-TIME:20240320T121500Z
DTEND;VALUE=DATE-TIME:20240320T130000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/47
DESCRIPTION:Title: Sampling on manifolds via Langevin diffusion\nby Akash Sharma (Chalm
ers University of Technology & University of Gothenburg) as part of Gothen
burg statistics seminar\n\nLecture held in MVL14.\n\nAbstract\nWe derive e
rror bounds for sampling and estimation using a discretization of an intri
nsically defined Langevin diffusion on a compact Riemannian manifold. Two
estimators of linear functionals of invariant measure based on the discret
ized Markov process are considered: a time-averaging estimator and an ense
mble-averaging estimator. Imposing no restrictions beyond a nominal level
of smoothness on potential function\, first-order error bounds\, in discre
tization step size\, on the bias and variances of both estimators are deri
ved. We will also discuss conditions for extending analysis to the case of
non-compact manifolds and different variants of the algorithm. We will pr
esent numerical illustrations with distributions on the manifolds of posit
ive and negative curvature which verify the derived bounds.\n\nJoint work
with Karthik Bharath (University of Nottingham)\, Alexander Lewis (Univers
ity of Gottingen) and Michael Tretyakov (University of Nottingham)\n
LOCATION:https://researchseminars.org/talk/gbgstats/47/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Nathan Gillot (University of Lorraine)
DTSTART;VALUE=DATE-TIME:20240403T111500Z
DTEND;VALUE=DATE-TIME:20240403T120000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/48
DESCRIPTION:Title: Modelling of the spread of a pathogen in coniferous forests and modellin
g for cosmological data characterization\nby Nathan Gillot (University
of Lorraine) as part of Gothenburg statistics seminar\n\nLecture held in
MVL14.\n\nAbstract\nAs stated in the title\, this presentation will be div
ided into two parts. The first will deal with work on epidemiological data
in a coniferous forest. We carried out modeling by considering probabilit
y laws on a lattice\, used the Gibbs sampler for simulation and used two p
arameter approximation methods for these models: pseudo-likelihood and a B
ayesian method\, the ABC Shadow algorithm. The second part of the talk wil
l focus on cosmological data. This time\, modeling will be done by spatial
point processes\, simulation by the Metropolis Hastings algorithm and inf
erence again by the ABC Shadow algorithm. The aim of this presentation is
to give an idea of the tools used for the modelling\, simulation and infer
ence process for these two projects.\n
LOCATION:https://researchseminars.org/talk/gbgstats/48/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Sergei Zuyev (Chalmers)
DTSTART;VALUE=DATE-TIME:20240417T111500Z
DTEND;VALUE=DATE-TIME:20240417T120000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/49
DESCRIPTION:Title: Training Bayesian neural networks with measure optimisation algorithms\nby Sergei Zuyev (Chalmers) as part of Gothenburg statistics seminar\n\
nLecture held in MVL14.\n\nAbstract\nOn a high abstraction level\, a Bayes
ian neural network (BNN) can be seen as a function\nof input data and thei
r prior probability distribution which yields\,\namong other outputs\, the
ir estimated posterior probability\ndistribution. This distribution is a r
esult of optimisation of a\nchosen score function aiming to favour these p
robability distributions\nwhich describe best the observed data and take i
nto account the prior\ndistribution.\n\nInstead of constraint optimisation
over the simplex of probability distributions\, it is typical to\nmap thi
s simplex into Euclidean space\, for example with Softmax function or its
variants\, and then do optimisation in the whole\nspace without constrain
ts. It is\, however\, widely acknowledged that such mapping often suffers
from undesirable properties for optimisation and\nstability of the algorit
hms. To counterfeit this\, a few regularisation procedures have been propo
sed in the literature.\n\nInstead of trying to modify the mapping approac
h\, we suggest\nturning back to optimisation on the original simplex using
recently\ndeveloped algorithms for constrained optimisation of functional
s of measures. \nWe demonstrate that our algorithms run tens times faster\
nthan the standard algorithms involving softmax mapping and lead to exact
solutions rather than to their approximations.\n
LOCATION:https://researchseminars.org/talk/gbgstats/49/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Radu Stoica (Université de Lorraine)
DTSTART;VALUE=DATE-TIME:20240424T111500Z
DTEND;VALUE=DATE-TIME:20240424T120000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/51
DESCRIPTION:Title: Approximated inference for marked Gibbs point process\nby Radu Stoic
a (Université de Lorraine) as part of Gothenburg statistics seminar\n\nLe
cture held in MVL14.\n\nAbstract\nParameter estimation for point processes
is achieved via solving optimisation problems built using general strateg
ies. Three well established strategies are enumerated. The first consists
of considering contrast fuctions based on summary statistics. The second o
ne uses the pseudo-likelihood. And the third approximates the likelihood f
unction via Monte Carlo procedures. Each of these techniques has known adv
antages and drawbacks (Moler and Waagepetersen 2004\, van Lieshout 2001\,
2019).\n\nSampling point process posterior densities is an inference appro
ach deeply intertwinned wih the previous one\, since it allows simultaneou
s parameter estimation and statistical tests based on observations. The au
xiliary variable method (Moller et al.\,2006) gives the mathematical solut
ion to this problem\, while pointing out the difficulties of its practical
implementation due to poor mixing. The exchange algorithm proposed by (Mu
rray et al. 2006)\, (Caimo and Friel\, 2011) proposes a solution for the p
oor mixing induced by the auxiliary variable method. As its predecessor it
requires exact simulation for the sampling of the auxiliary variable. Thi
s is not really a drawback\, but it may explode the computational time for
models exhibiting strong interactions (van Lieshout and Stoica\, 2006). \
n\nThis talk presents the approximate ABC Shadow and SSA methods as comple
mentary inference methods to the ones based on posterior density sampling.
These methods do not require exact simulation\, while providing the neces
sary theoretical control. The derived algorithms are applied on data from
several application domains such as astronomy\, geosciences and network s
ciences (Stoica et al.\,17)\, (Stoica et al.\,21)\, (Hurtado et al.\,21)\,
(Laporte et al.\,22).\n
LOCATION:https://researchseminars.org/talk/gbgstats/51/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Robert Berman (Chalmers University of Technology & University of G
othenburg)
DTSTART;VALUE=DATE-TIME:20240508T111500Z
DTEND;VALUE=DATE-TIME:20240508T120000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/53
DESCRIPTION:Title: Emergent complex geometry\nby Robert Berman (Chalmers University of
Technology & University of Gothenburg) as part of Gothenburg statistics se
minar\n\nLecture held in MVL14.\n\nAbstract\nA recurrent theme in geometry
is the quest for canonical metrics on a given manifold X. The prototypica
l case is when X is a compact orientable two-dimensional surface. Such a m
anifold can be endowed with a metric of constant curvature\, which is uniq
uely determined by a fixing a complex structure on X. However\, from a phy
sical point of view\, geometrical shapes - as we know them from everyday e
xperience - are\, of course\, not fundamental physical entities. They mere
ly arise as macroscopic emergent features of ensembles of microscopic poin
t particles in the limit as the number N of particles tends to infinity. T
his leads one to wonder if there is a canonical random point process on a
given complex manifold X\, from which a canonical metrics emerges as the n
umber N of points tends to infinity? This is\, indeed\, the case\, when X
is a complex algebraic hypersurface of any dimension\, as explained in the
present talk. In this case the emerging metrics in question have constant
Ricci curvature. More precisely\, they are Kähler-Einstein metrics. The
talk is aimed to be non-technical and no previous background in complex ge
ometry is required.\n
LOCATION:https://researchseminars.org/talk/gbgstats/53/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Umberto Picchini (Chalmers University of Technology & University o
f Gothenburg)
DTSTART;VALUE=DATE-TIME:20240515T111500Z
DTEND;VALUE=DATE-TIME:20240515T120000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/54
DESCRIPTION:Title: Fast\, lightweight and semi-amortised simulation-based inference\nby
Umberto Picchini (Chalmers University of Technology & University of Gothe
nburg) as part of Gothenburg statistics seminar\n\nLecture held in MVL14.\
n\nAbstract\nBayesian inference for complex models with an intractable lik
elihood can be tackled using algorithms performing many calls to computer
simulators. These approaches are collectively known as "simulation-based i
nference" (SBI). Recent SBI methods use neural-conditional-estimation\, th
at is neural networks are employed to provide approximations to the likeli
hood function or the posterior distribution of model parameters. While neu
ral-based posterior and likelihood estimation methods have produced except
ionally flexible inference strategies\, these can be computationally inten
sive to run and have a non-negligible impact on energy expenditure and mem
ory requirements. In this work\, rather than using neural networks we prop
ose more "frugal" strategies that display state-of-art inference quality\,
while being able to run with limited resources\, being much faster to tra
in and exhibiting a much smaller computational footprint. We investigate s
tructured mixtures of probability distributions and design a new SBI metho
d named Sequential Mixture Posterior and Likelihood Estimation (SeMPLE). S
eMPLE learns closed-form approximations for both the posterior $p(θ|y)$ a
nd the likelihood $p(y|θ)$ from the same training data\, using Gaussian m
ixture models that can be efficiently learned.\nWe show favorable results
for a variety of stochastic models (including SDEs and Markov jump process
es)\, also in presence of multimodal posteriors. \n\nThe talk will be appr
oachable for the uninitiated audience\, while novel results will be of int
erest for the experienced audience.\n\nJoint work with Henrik Häggström\
, Pedro L. C. Rodrigues\, Geoffroy Oudoumanessah and Florence Forbes\, htt
ps://arxiv.org/abs/2403.07454\n
LOCATION:https://researchseminars.org/talk/gbgstats/54/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Adrien Corenflos (University of Warwick)
DTSTART;VALUE=DATE-TIME:20240529T111500Z
DTEND;VALUE=DATE-TIME:20240529T120000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/57
DESCRIPTION:Title: Particle-MALA and Particle-mGrad: Gradient-based MCMC methods for high-d
imensional state-space models\nby Adrien Corenflos (University of Warw
ick) as part of Gothenburg statistics seminar\n\nLecture held in MVL14.\n\
nAbstract\nState-of-the-art methods for Bayesian inference in state-space
models are (a) conditional sequential Monte Carlo (CSMC) algorithms\; (b)
sophisticated 'classical' MCMC algorithms like MALA\, or mGRAD from Titsia
s and Papaspiliopoulos (2018). The former propose N particles at each time
step to exploit the model's 'decorrelation-over-time' property and thus s
cale favourably with the time horizon\, T\, but break down if the dimensio
n of the latent states\, D\, is large. The latter leverage gradient/prior-
informed local proposals to scale favourably with D but exhibit sub-optima
l scalability with T due to a lack of model-structure exploitation. We int
roduce methods which combine the strengths of both approaches. The first\,
Particle-MALA\, spreads N particles locally around the current state usin
g gradient information\, thus extending MALA to T>1 time steps and N>1 pro
posals. The second\, Particle-mGRAD\, additionally incorporates (condition
ally) Gaussian prior dynamics into the proposal\, thus extending the mGRAD
algorithm. We prove that Particle-mGRAD interpolates between CSMC and Par
ticle-MALA\, resolving the 'tuning problem' of choosing between CSMC (supe
rior for highly informative prior dynamics) and Particle-MALA (superior fo
r weakly informative prior dynamics). We similarly extend other 'classical
' MCMC approaches like auxiliary MALA\, aGRAD\, and preconditioned Crank-N
icolson-Langevin (PCNL). In experiments\, our methods substantially improv
e upon both CSMC and sophisticated `classical' MCMC approaches for both hi
ghly and weakly informative prior dynamics.\n\nTL\;DR: We aim to solve the
curse of dimensionality in state-space model inferences by combining the
nice property (in time) of conditional particle filtering methods\, with t
he nice property (in space) of MALA and other gradient-based algorithms.\n
LOCATION:https://researchseminars.org/talk/gbgstats/57/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Helga Olafsdottir (Chalmers University of Technology & University
of Gothenburg)
DTSTART;VALUE=DATE-TIME:20240821T111500Z
DTEND;VALUE=DATE-TIME:20240821T120000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/62
DESCRIPTION:Title: Scoring rule inference for spatial statistics based on cross-validation<
/a>\nby Helga Olafsdottir (Chalmers University of Technology & University
of Gothenburg) as part of Gothenburg statistics seminar\n\nLecture held in
MVF21 (sic!).\n\nAbstract\nAlthough scoring rules are traditionally aimed
at model evaluation\, they have also successfully been used for model inf
erence. We propose parameter inference of spatial models through a leave-o
ne-out cross-validation approach (LOOS)\, where the predictive ability is
optimised instead of the likelihood. The approach is studied for different
Gaussian spatial models. For Gaussian models with sparse precision matric
es\, such as spatial Markov models\, the approach results in fast computat
ions compared to the likelihood approach. Moreover\, the approach allows a
ffecting the robustness to outliers and sensitivity to non-stationarity. A
pplying the LOOS to ERA5 temperature reanalysis data for the contiguous Un
ited States and the average July temperature for the years 1940 to 2023 re
sulted in estimates with better predictive performance than the maximum li
kelihood in a fraction of the computation time.\n
LOCATION:https://researchseminars.org/talk/gbgstats/62/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Erik Jansson (Chalmers University of Technology and University of
Gothenburg)
DTSTART;VALUE=DATE-TIME:20240828T111500Z
DTEND;VALUE=DATE-TIME:20240828T120000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/63
DESCRIPTION:Title: Sampling non-stationary Gaussian random fields on hypersurfaces using su
rface finite element methods\nby Erik Jansson (Chalmers University of
Technology and University of Gothenburg) as part of Gothenburg statistics
seminar\n\nLecture held in MVL14.\n\nAbstract\nIn the middle of the previo
us century\, Peter Whittle demonstrated that Whittle–Matérn Gaussian\nr
andom fields on Euclidean domains can be obtained as solutions to fraction
al elliptic stochastic\npartial differential equations (SPDEs). The SPDE
–random field connection can be leveraged to\ngenerate random fields on
other domains\, such as curves or surfaces\, by solving an SPDE on\nthat d
omain. Selecting a differential operator with variable coefficients\, we o
btain a flexible\nclass of non-stationary random fields. We consider how t
he computational technique of surface\nfinite elements can be utilized to
sample random fields on surfaces and briefly discuss how\nstrong error bou
nds are obtained using complex analysis and operator theory. \nThis talk
is based on joint work with Annika Lang and Mike Pereira.\n
LOCATION:https://researchseminars.org/talk/gbgstats/63/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Peter Guttorp (Norsk Regnesentral)
DTSTART;VALUE=DATE-TIME:20241113T121500Z
DTEND;VALUE=DATE-TIME:20241113T130000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/64
DESCRIPTION:Title: Water is warmer than air\, so why do we use sea surface temperature to e
stimate global temperature?\nby Peter Guttorp (Norsk Regnesentral) as
part of Gothenburg statistics seminar\n\nLecture held in MVL14.\n\nAbstrac
t\nIn the study of global climate\, ocean temperature estimates use sea su
rface temperature (SST) anomalies instead of marine atmospheric temperatur
e (MAT) anomalies. A key question is to ask what biases result from this c
hoice. In this talk we employ hierarchical statistical models to investiga
te spatial-temporal differences between SST and MAT anomalies in the tropi
cal Pacific. The analysis uses observations from the Tropical Atmosphere O
cean (TAO) buoy network. We use a spatio-temporal modeling approach that a
ccounts for missing data in the observation network\, and allows for full
uncertainty quantification. We also compare our results to another analys
is that uses data from the European Center for Medium Range Weather Foreca
sting fifth generation reanalysis product (ERA5). We find no indication of
bias or trend in replaciing MAT by SST in calculating global temperature
anomalies.\n
LOCATION:https://researchseminars.org/talk/gbgstats/64/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Satish Iyengar (Department of Statistics\, University of Pittsburg
h\, Pittsburgh\, PA\, USA)
DTSTART;VALUE=DATE-TIME:20240925T111500Z
DTEND;VALUE=DATE-TIME:20240925T120000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/65
DESCRIPTION:Title: A clustering problem arising in psychiatry\nby Satish Iyengar (Depar
tment of Statistics\, University of Pittsburgh\, Pittsburgh\, PA\, USA) as
part of Gothenburg statistics seminar\n\nLecture held in MVL14.\n\nAbstra
ct\nCurrent psychiatric diagnoses are based primarily on self-reported exp
eriences. Unfortunately\, treatments for the diagnoses are not effective f
or all patients. One hypothesized reason is that ``artificial grouping of
heterogeneous syndromes with different pathophysiological mechanisms into
one disorder.'' To address this problem\, the US National Institute of Men
tal Health instituted the Research Domain Criteria framework in 2009. This
research framework calls for integrating data from many levels of informa
tion: genes\, cells\, molecules\, circuits\, physiology\, behavior\, and s
elf-report. Clustering comes to the forefront as a key tool in this effort
. In this talk\, I present a case study of the use of mixture models to cl
uster older adults based on measures of sleep from three domains: diary\,
actigraphy\, and polysomnography. Challenges in this effort include the us
e of mixtures of skewed distributions\, a large number of potential cluste
ring variables\, and seeking clinically meaningful solutions. We present n
ovel variable selection algorithms\, study them by simulation\, and demons
trate our methods on the sleep data. This work is joint with Meredith Wall
ace.\n
LOCATION:https://researchseminars.org/talk/gbgstats/65/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Linn Engström (KTH Royal Institute of Technology)
DTSTART;VALUE=DATE-TIME:20241023T111500Z
DTEND;VALUE=DATE-TIME:20241023T120000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/68
DESCRIPTION:Title: Computation of Robust Option Prices via Martingale Optimal Transport
\nby Linn Engström (KTH Royal Institute of Technology) as part of Gothenb
urg statistics seminar\n\nLecture held in MVL14.\n\nAbstract\nDuring the l
ast decade there has been a rapid development of methods for computational
ly addressing optimal transport problems\; motivated by applications withi
n robust finance\, effort has also been made to generalize some of these t
echniques to problems equipped with an additional martingale constraint. C
omputationally solving multi-marginal martingale optimal transport problem
s remains a challenging task though\, particularly for problems formulated
with a large number of marginals.\n\nIn this talk I will give a brief int
roduction to the martingale optimal transport problem and motivate why it
is interesting from a mathematical finance point of view\, before presenti
ng an efficient framework for solving a class of such multi-marginal probl
ems computationally. The method combines the celebrated entropic regulariz
ation with the exploitation of certain structures inherent in the problem\
, enabling fast computation of the optimal dual variables. I will also pro
vide some examples that demonstrates the utility of our method in terms of
computing model-independent bounds on the fair price of some exotic optio
ns\, such as lookback options and Asian options. The talk is based on join
t work with Sigrid Källblad and Johan Karlsson.\n
LOCATION:https://researchseminars.org/talk/gbgstats/68/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Marcel Wienöbst (University of Lübeck)
DTSTART;VALUE=DATE-TIME:20241106T090000Z
DTEND;VALUE=DATE-TIME:20241106T094500Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/70
DESCRIPTION:Title: Linear-Time Algorithms for Front-Door Adjustment in Causal Graphs\nb
y Marcel Wienöbst (University of Lübeck) as part of Gothenburg statistic
s seminar\n\nLecture held in MVL14.\n\nAbstract\nCausal effect estimation
from observational data is a fundamental task in\nempirical sciences. It b
ecomes particularly challenging when unobserved\nconfounders are involved
in a system. Front-door adjustment constitutes a\nclassic method that allo
ws identifying the causal effect even in the presence of\nlatent confoundi
ng by using observed mediators. This talk presents a recent\nalgorithmic r
esult in this area\, namely a linear-time algorithm for finding a\nfront-d
oor adjustment set in a given causal graph. Its run-time is\nasymptoticall
y optimal and improves on the previous state-of-the-art for this\ntask by
a factor that grows cubically in the number of variables. Beyond this\nres
ult\, the presentation explores fundamental algorithmic tools and techniqu
es\nuseful for broader applications in causal inference.\n
LOCATION:https://researchseminars.org/talk/gbgstats/70/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Sebastian Persson
DTSTART;VALUE=DATE-TIME:20241127T121500Z
DTEND;VALUE=DATE-TIME:20241127T130000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/72
DESCRIPTION:by Sebastian Persson as part of Gothenburg statistics seminar\
n\nLecture held in MVL14.\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/gbgstats/72/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Lembris Njotto (University of Dar es Salaam)
DTSTART;VALUE=DATE-TIME:20241120T121500Z
DTEND;VALUE=DATE-TIME:20241120T130000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/73
DESCRIPTION:by Lembris Njotto (University of Dar es Salaam) as part of Got
henburg statistics seminar\n\nLecture held in MVL14.\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/gbgstats/73/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Martin Voigt Vejling (Aalborg University)
DTSTART;VALUE=DATE-TIME:20241120T100000Z
DTEND;VALUE=DATE-TIME:20241120T110000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/74
DESCRIPTION:Title: Conformal multiple Monte Carlo testing with a view to spatial statistics
\nby Martin Voigt Vejling (Aalborg University) as part of Gothenburg s
tatistics seminar\n\nLecture held in MVL14.\n\nAbstract\nMonte Carlo tests
are popular for their convenience\, as they allow the computation of vali
d p-values even when test statistics with known and tractable distribution
s are unavailable. When performing multiple Monte Carlo tests\, it is esse
ntial to adjust the testing procedure to maintain control of the type I er
ror\, and some of such techniques pose requirements on the joint distribut
ion of the p-values\, for instance independence. A straightforward approac
h to get independent p-values\, is to compute the p-values for each hypoth
esis in parallel\, however\, this imposes a substantial computational burd
en. We highlight in this work that the problem of testing multiple data sa
mples against the same null hypothesis is an instance of conformal outlier
detection. Leveraging this insight enables a more efficient multiple Mont
e Carlo testing procedure\, avoiding excessive simulations while still ens
uring exact control over the false discovery rate. Through numerical exper
iments on point patterns we investigate the performance of this proposed c
onformal multiple Monte Carlo testing (CMMCTest) procedure.\n
LOCATION:https://researchseminars.org/talk/gbgstats/74/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Aila Särkkä (Chalmers University of Technology & University of G
othenburg)
DTSTART;VALUE=DATE-TIME:20241211T121500Z
DTEND;VALUE=DATE-TIME:20241211T130000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/75
DESCRIPTION:Title: Analysis of point patterns observed with errors: some examples\nby A
ila Särkkä (Chalmers University of Technology & University of Gothenburg
) as part of Gothenburg statistics seminar\n\nLecture held in MVL14.\n\nAb
stract\nMany natural systems are observed as point patterns in time\, spac
e\, or space and time. Examples include plant and cellular systems\, anima
l colonies\, wildfires\, and galaxies. In practice\, the locations of the
points are not always observed correctly. However\, in the point process l
iterature\, little attention has been paid to the issue of errors in the l
ocations of points. In this talk\, we discuss how the observed point patte
rn may deviate from the actual point pattern\, review methods and models t
hat exist to handle such deviations\, and give some examples of data obser
ved with errors. \n\nBased on joint work with Peter Guttorp (Norwegian Com
puting Center)\, Janine Illian (University of Glasgow)\, Joel Kostensalo (
Natural Resources Institute Finland (Luke)\, Mikko Kuronen (Luke)\, Mari M
yllymäki (Luke)\, and Thordis Thorarinsdottir (University of Oslo).\n
LOCATION:https://researchseminars.org/talk/gbgstats/75/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Henrik Häggström
DTSTART;VALUE=DATE-TIME:20241204T121500Z
DTEND;VALUE=DATE-TIME:20241204T130000Z
DTSTAMP;VALUE=DATE-TIME:20241112T124935Z
UID:gbgstats/76
DESCRIPTION:by Henrik Häggström as part of Gothenburg statistics seminar
\n\nLecture held in MVL14.\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/gbgstats/76/
END:VEVENT
END:VCALENDAR