Theses and Dissertations

Author

Xiaojian Wu

Issuing Body

Mississippi State University

Advisor

Yuan, Changhe

Committee Member

Hansen, Eric A.

Committee Member

Stocker, Russell

Date of Degree

1-1-2010

Document Type

Graduate Thesis - Open Access

Degree Name

Master of Science

College

James Worth Bagley College of Engineering

Department

Department of Computer Science and Engineering

Abstract

Bayesian network is a popular machine learning tool for modeling uncertain dependence relationships among the random factors of a domain. It represents the relations qualitatively by using a directed acyclic graph (DAG) and quantitatively by using a set of conditional probability distributions. Several exact algorithms for learning optimal Bayesian networks from data have been developed recently. However, these algorithms are still inefficient to some extent. This is not surprising because learning Bayesian network has been proven to be an NP-Hard problem. Based on a critique of these algorithms, this thesis introduces a new algorithm based on heuristic search for learning optimal Bayesian.

URI

https://hdl.handle.net/11668/17868

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