Efficient Bayesian Network Inference: Genetic Algorithms, Stochastic Local Search, and Abstraction
Mengshoel, Ole Jakob
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https://hdl.handle.net/2142/81946
Description
Title
Efficient Bayesian Network Inference: Genetic Algorithms, Stochastic Local Search, and Abstraction
Author(s)
Mengshoel, Ole Jakob
Issue Date
1999
Doctoral Committee Chair(s)
Wilkins, David C.
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Computer Science
Language
eng
Abstract
Two major research results are presented that relate to creating hard synthetic Bayesian networks for empirical research on inference algorithms. One method translates deceptive problems studied in genetic algorithms to a Bayesian network setting, showing that Bayesian networks can be deceptive. The other result is based on translating satisfiability problems into Bayesian networks. We describe how connectivity, value of conditional probability tables as well as the degree of regularity of the underlying graph affect the speed of inference for Hugin and Stochastic Greedy Search.
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