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A BIRTH AND DEATH PROCESS FOR BAYESIAN NETWORK STRUCTURE INFERENCE

Published online by Cambridge University Press:  26 December 2017

Dale Jennings  and
Jem N. Corcoran
Dale Jennings
Affiliation:
Applied Mathematics, University of Colorado, 526 UCB, Boulder, Colorado, USA E-mail: [email protected]
Jem N. Corcoran
Affiliation:
Applied Mathematics, University of Colorado, 526 UCB, Boulder, Colorado, USA E-mail: [email protected]
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Abstract

Bayesian networks are convenient graphical expressions for high-dimensional probability distributions which represent complex relationships between a large number of random variables. They have been employed extensively in areas such as bioinformatics, artificial intelligence, diagnosis, and risk management. The recovery of the structure of a network from data is of prime importance for the purposes of modeling, analysis, and prediction. There has been a great deal of interest in recent years in the NP-hard problem of learning the structure of a Bayesian network from observed data. Typically, one assigns a score to various structures and the search becomes an optimization problem that can be approached with either deterministic or stochastic methods. In this paper, we introduce a new search strategy where one walks through the space of graphs by modeling the appearance and disappearance of edges as a birth and death process. We compare our novel approach with the popular Metropolis–Hastings search strategy and give empirical evidence that the birth and death process has superior mixing properties.

Keywords

Bayesian networksbirth and death processesstructure learning
Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 32 , Issue 4 , October 2018 , pp. 615 - 625
Copyright
Copyright © Cambridge University Press 2017 

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