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SUMMARY:Moritz Schauer (Chalmers University of Technology & University of 
 Gothenburg)
DTSTART:20231020T090000Z
DTEND:20231020T094500Z
DTSTAMP:20260422T155052Z
UID:gbgstats/37
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/gbgstats/37/
 ">Causal structure learning and sampling using Markov Monte Carlo with mom
 entum</a>\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/
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