Files in this item

FilesDescriptionFormat

application/pdf

application/pdf9944937.pdf (10MB)Restricted to U of Illinois
(no description provided)PDF

Description

Title:Efficient Bayesian Network Inference: Genetic Algorithms, Stochastic Local Search, and Abstraction
Author(s):Mengshoel, Ole Jakob
Doctoral Committee Chair(s):Wilkins, David C.
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Computer Science
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.
Issue Date:1999
Type:Text
Language:English
Description:210 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1999.
URI:http://hdl.handle.net/2142/81946
Other Identifier(s):(MiAaPQ)AAI9944937
Date Available in IDEALS:2015-09-25
Date Deposited:1999


This item appears in the following Collection(s)

Item Statistics