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Finding Optimal Bayesian Networks by Dynamic Programming (2005)

Ajit Singh and Andrew Moore

Abstract

Finding the Bayesian network that maximizes a score function is known as {\it structure
learning} or {\it structure discovery}. Most approaches use local search in the space of
acyclic digraphs, which is prone to local maxima. Exhaustive enumeration requires
super-exponential time. In this paper we describe a ``merely" exponential space/time algorithm
for finding a Bayesian network that corresponds to a global maxima of a decomposable
scoring function, such as BDeu or BIC.

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Approximate BibTeX Entry

@techreport{singh:04,
    Month = {June},
    Year = {2005},
    Pages = {16},
    School = {School of Computer Science},
    Institution = {Carnegie Mellon University},
    Title = {Finding Optimal Bayesian Networks by Dynamic Programming}
}