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University of Illinois at Urbana-Champaign
Efficient Bayesian Network Inference: Genetic Algorithms, Stochastic Local Search, and Abstraction
Abstract
dc:descriptionTwo 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.
Degree
thesis:*- Name thesis:degree_name
- Ph.D.
- Level thesis:degree_level
- Dissertation
- Discipline thesis:degree_discipline
- Computer Science
- Grantor
- University of Illinois at Urbana-Champaign
- Year dc:date
- 2015
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Mengshoel, Ole Jakob
- Contributors dc:contributor
-
- Wilkins, David C.
Subjects
dc:subject × 1Rights
- Language dc:language
- eng
Identifiers
dc:identifier.*- Identifier
- (MiAaPQ)AAI9944937
- OAI identifier oai:identifier
- oai:www.ideals.illinois.edu:2142/81946