Learning equivalence classes of bayesian-network structures

TLDR: In this paper, the authors consider using a score equivalent criterion in conjunction with a heuristic search algorithm to perform model selection or model averaging, and show that more sophisticated search algorithms are likely to benefit much more.

Abstract: Two Bayesian-network structures are said to be equivalent if the set of distributions that can be represented with one of those structures is identical to the set of distributions that can be represented with the other. Many scoring criteria that are used to learn Bayesian-network structures from data are score equivalent; that is, these criteria do not distinguish among networks that are equivalent. In this paper, we consider using a score equivalent criterion in conjunction with a heuristic search algorithm to perform model selection or model averaging. We argue that it is often appropriate to search among equivalence classes of network structures as opposed to the more common approach of searching among individual Bayesian-network structures. We describe a convenient graphical representation for an equivalence class of structures, and introduce a set of operators that can be applied to that representation by a search algorithm to move among equivalence classes. We show that our equivalence-class operators can be scored locally, and thus share the computational efficiency of traditional operators defined for individual structures. We show experimentally that a greedy model-selection algorithm using our representation yields slightly higher-scoring structures than the traditional approach without any additional time overhead, and we argue that more sophisticated search algorithms are likely to benefit much more.