Learning Optimal Bayesian Networks: A Shortest Path Perspective

Changhe Yuan and Brandon Malone

Abstract

In this paper, learning a Bayesian network structure that optimizes a scoring function for a given dataset is viewed as a shortest path problem in an implicit state-space search graph. This perspective highlights the importance of two research issues: the development of search strategies for solving the shortest path problem, and the design of heuristic functions for guiding the search. This paper introduces several techniques for addressing the issues. One is an A* search algorithm that learns an optimal Bayesian network structure by only searching the most promising part of the solution space. The others are mainly two heuristic functions. The first heuristic function represents a simple relaxation of the acyclicity constraint of a Bayesian network. Although admissible and consistent, the heuristic may introduce too much relaxation and result in a loose bound. The second heuristic function reduces the amount of relaxation by avoiding directed cycles within some groups of variables. Empirical results show that these methods constitute a promising approach to learning optimal Bayesian network structures.

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Bibtex

@ARTICLE{Yuan13learning, author = {Changhe Yuan and Brandon Malone},
title = {Learning Optimal {B}ayesian Networks: A Shortest Path Perspective},
journal = {Journal of Artificial Intelligence Research (JAIR)},
year = {2013},
volume = {48},
pages = {23--65}
}