Theses and Dissertations
Issuing Body
Mississippi State University
Advisor
Yuan, Changhe
Committee Member
Hansen, Eric A.
Committee Member
Stocker, Russell
Date of Degree
8-7-2010
Document Type
Graduate Thesis - Open Access
Major
Computer Science
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
Recommended Citation
Wu, Xiaojian, "A Heuristic Search Algorithm for Learning Optimal Bayesian Networks" (2010). Theses and Dissertations. 154.
https://scholarsjunction.msstate.edu/td/154