A BIRTH AND DEATH PROCESS FOR BAYESIAN NETWORK STRUCTURE INFERENCE. Issue 4 (26th December 2017)

• Record Type: Journal Article
• Authors: Jennings, Dale; Corcoran, Jem N.
• Is Part Of: Probability in the engineering and informational sciences. Volume 32:Issue 4(2018)
• Page Start: 615
• Page End: 625
• Publication Date: 2017-12-26
• Subjects: Bayesian networks, -- birth and death processes, -- structure learning; Probabilities -- Periodicals; Engineering -- Statistical methods -- Periodicals; Information science -- Statistical methods -- Periodicals; 519.202462
• DOI: 10.1017/S0269964817000432
• Languages: English
• ISSNs: 0269-9648

Abstract

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.