On sampling graphical Markov models

Abstract

We consider sampling and enumeration problems for Markov equivalence classes. We create and analyze a Markov chain for uniform random sampling on the DAGs inside a Markov equivalence class. Though the worst case is exponentially slow mixing, we find a condition on the Markov equivalence class for polynomial time mixing. We also investigate the ratio of Markov equivalence classes to DAGs and a Markov chain of He, Jia, and Yu for random sampling of sparse Markov equivalence classes.

References in corpus (1)

• Estimating high-dimensional intervention effects from observational data

Cited by in corpus (2)

• A Local Method for Identifying Causal Relations under Markov Equivalence
• Interventional Experiment Design for Causal Structure Learning