Causal structure learning and sampling using Markov Monte Carlo with momentum

Abstract: In the context of inferring a Bayesian network structure from observational data, that is inferring a directed acyclic graph (DAG), we devise a non-reversible continuous-time Markov chain that targets a probability distribution over classes of observationally equivalent (Markov equivalent) DAGs. The classes are represented as completed partially directed acyclic graphs (CPDAGs). The non-reversible Markov chain relies on the operators used in Chickering’s Greedy Equivalence Search (GES) and is endowed with a momentum variable, which improves mixing significantly as we show empirically. The possible target distributions include posterior distributions based on a prior and a Markov equivalent likelihood. Joint work with Marcel Wienöbst (Universität zu Lübeck).

machine learningprobabilitystatistics theory

Audience: researchers in the discipline

( paper | slides )

Comments: This is a talk in the webinar series of the Cramér society

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Gothenburg statistics seminar

Series comments: Gothenburg statistics seminar is open to the interested public, everybody is welcome. It usually takes place in MVL14 (http://maps.chalmers.se/#05137ad7-4d34-45e2-9d14-7f970517e2b60, see specific talk). Speakers are asked to prepare material for 35 minutes excluding questions from the audience.

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