SparsityBoost: A new scoring function for learning Bayesian network structure

Eliot Brenner, David Sontag

Research output: Contribution to conferencePaper

Abstract

We give a new consistent scoring function for structure learning of Bayesian networks. In contrast to traditional approaches to score-based structure learning, such as BDeu or MDL, the complexity penalty that we propose is data-dependent and is given by the probability that a conditional independence test correctly shows that an edge cannot exist. What really distinguishes this new scoring function from earlier work is that it has the property of becoming computationally easier to maximize as the amount of data increases. We prove a polynomial sample complexity result, showing that maximizing this score is guaranteed to correctly learn a structure with no false edges and a distribution close to the generating distribution, whenever there exists a Bayesian network which is a perfect map for the data generating distribution. Although the new score can be used with any search algorithm, we give empirical results showing that it is particularly effective when used together with a linear programming relaxation approach to Bayesian network structure learning.

Original languageEnglish (US)
Pages112-121
Number of pages10
StatePublished - Nov 28 2013
Event29th Conference on Uncertainty in Artificial Intelligence, UAI 2013 - Bellevue, WA, United States
Duration: Jul 11 2013Jul 15 2013

Other

Other29th Conference on Uncertainty in Artificial Intelligence, UAI 2013
CountryUnited States
CityBellevue, WA
Period7/11/137/15/13

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ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Brenner, E., & Sontag, D. (2013). SparsityBoost: A new scoring function for learning Bayesian network structure. 112-121. Paper presented at 29th Conference on Uncertainty in Artificial Intelligence, UAI 2013, Bellevue, WA, United States.