Bayesian structure learning using dynamic programming and MCMC

Understand

MCMC methods for sampling from the space of DAGs can mix poorly due to the local nature of the proposals that are commonly used.

• It has been shown that sampling from the space of node orders yields better results [FK03, EW06].
• Recently, Koivisto and Sood showed how one can analytically marginalize over orders using dynamic programming (DP) [KS04, Koi06].
• Their method computes the exact marginal posterior edge probabilities, thus avoiding the need for MCMC.