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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:20230331T091659Z
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:20230331T091659Z
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:20230331T091659Z
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:20230331T091659Z
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:20230331T091659Z
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:20230331T091659Z
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:20230331T091659Z
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:20230331T091659Z
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:20230331T091659Z
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:20230331T091659Z
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:20230331T091659Z
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:20230331T091659Z
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:20230331T091659Z
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:TBA
DTSTART;VALUE=DATE-TIME:20230209T121500Z
DTEND;VALUE=DATE-TIME:20230209T130000Z
DTSTAMP;VALUE=DATE-TIME:20230331T091659Z
UID:gbgstats/14
DESCRIPTION:by TBA as part of Gothenburg statistics seminar\n\nLecture hel
d in MVL15.\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/gbgstats/14/
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:20230331T091659Z
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:20230331T091659Z
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:20230331T091659Z
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:20230331T091659Z
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:TBA
DTSTART;VALUE=DATE-TIME:20230323T121500Z
DTEND;VALUE=DATE-TIME:20230323T130000Z
DTSTAMP;VALUE=DATE-TIME:20230331T091659Z
UID:gbgstats/20
DESCRIPTION:by TBA as part of Gothenburg statistics seminar\n\nLecture hel
d in MVL14.\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/gbgstats/20/
END:VEVENT
BEGIN:VEVENT
SUMMARY:TBA
DTSTART;VALUE=DATE-TIME:20230330T111500Z
DTEND;VALUE=DATE-TIME:20230330T120000Z
DTSTAMP;VALUE=DATE-TIME:20230331T091659Z
UID:gbgstats/21
DESCRIPTION:by TBA as part of Gothenburg statistics seminar\n\nLecture hel
d in MVL14.\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/gbgstats/21/
END:VEVENT
BEGIN:VEVENT
SUMMARY:TBA
DTSTART;VALUE=DATE-TIME:20230413T111500Z
DTEND;VALUE=DATE-TIME:20230413T120000Z
DTSTAMP;VALUE=DATE-TIME:20230331T091659Z
UID:gbgstats/22
DESCRIPTION:by TBA as part of Gothenburg statistics seminar\n\nLecture hel
d in MVL14.\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/gbgstats/22/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Reserved
DTSTART;VALUE=DATE-TIME:20230420T111500Z
DTEND;VALUE=DATE-TIME:20230420T120000Z
DTSTAMP;VALUE=DATE-TIME:20230331T091659Z
UID:gbgstats/23
DESCRIPTION:by Reserved as part of Gothenburg statistics seminar\n\nLectur
e held in MVL14.\nAbstract: TBA\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:20230331T091659Z
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
DTSTART;VALUE=DATE-TIME:20230504T111500Z
DTEND;VALUE=DATE-TIME:20230504T120000Z
DTSTAMP;VALUE=DATE-TIME:20230331T091659Z
UID:gbgstats/25
DESCRIPTION:by Moa Johansson as part of Gothenburg statistics seminar\n\nL
ecture held in MVL14.\nAbstract: TBA\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:20230331T091659Z
UID:gbgstats/26
DESCRIPTION:by Oskar Allerbo (Chalmers University of Technology & Universi
ty of Gothenburg) as part of Gothenburg statistics seminar\n\nLecture held
in MVL14.\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/gbgstats/26/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Juan Salvador Inda Diaz (Chalmers University of Technology & Unive
rsity of Gothenburg)
DTSTART;VALUE=DATE-TIME:20230516T111500Z
DTEND;VALUE=DATE-TIME:20230516T120000Z
DTSTAMP;VALUE=DATE-TIME:20230331T091659Z
UID:gbgstats/27
DESCRIPTION:by Juan Salvador Inda Diaz (Chalmers University of Technology
& University of Gothenburg) as part of Gothenburg statistics seminar\n\nLe
cture held in MVL15.\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/gbgstats/27/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Frank Miller (Linköping University)
DTSTART;VALUE=DATE-TIME:20230525T111500Z
DTEND;VALUE=DATE-TIME:20230525T120000Z
DTSTAMP;VALUE=DATE-TIME:20230331T091659Z
UID:gbgstats/28
DESCRIPTION:by Frank Miller (Linköping University) as part of Gothenburg
statistics seminar\n\nLecture held in MVL14.\nAbstract: TBA\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:20230331T091659Z
UID:gbgstats/29
DESCRIPTION:by Lotta Eriksson (Chalmers University of Technology & Univers
ity of Gothenburg) as part of Gothenburg statistics seminar\n\nLecture hel
d in MVL14.\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/gbgstats/29/
END:VEVENT
END:VCALENDAR