Blankets Joint Posterior score for learning Markov network structures

TL;DR

This paper introduces the Blankets Joint Posterior score, a new method for learning Markov network structures that considers the joint distribution of Markov blankets, improving accuracy over previous independent-blanket assumptions.

Contribution

It proposes a scoring function that relaxes the independence assumption between Markov blankets, enabling more accurate structure learning of complex networks.

Findings

Improves sample complexity in learning complex networks

Outperforms state-of-the-art scores in experiments

Effectively models dependencies among blankets

Abstract

Markov networks are extensively used to model complex sequential, spatial, and relational interactions in a wide range of fields. By learning the structure of independences of a domain, more accurate joint probability distributions can be obtained for inference tasks or, more directly, for interpreting the most significant relations among the variables. Recently, several researchers have investigated techniques for automatically learning the structure from data by obtaining the probabilistic maximum-a-posteriori structure given the available data. However, all the approximations proposed decompose the posterior of the whole structure into local sub-problems, by assuming that the posteriors of the Markov blankets of all the variables are mutually independent. In this work, we propose a scoring function for relaxing such assumption. The Blankets Joint Posterior score computes the joint posterior of structures as a joint distribution of the collection of its Markov blankets. Essentially, the whole posterior is obtained by computing the posterior of the blanket of each variable as a conditional distribution that takes into account information from other blankets in the network. We show in our experimental results that the proposed approximation can improve the sample complexity of state-of-the-art scores when learning complex networks, where the independence assumption between blanket variables is clearly incorrect.