Statistics seminar

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).

Comments: This is a talk in the webinar series of the Cramér society, password: 197131