Heuristic search in symbolic probability inference

Graduate Project

公开 Deposited

Citeable URL: https://ir.library.oregonstate.edu/concern/graduate_projects/rn301861x

Descriptions

Attribute Name	Values
Creator	Pan, Yonghong
Abstract	I present a new heuristic search approach to compute approximate answers for the probability query in belief nets. This approach can compute the 'best' bounds for a query in a period of any given time (if time permitted, it will get an exact value). It inherits the essence of Symbolic Probabilistic Inference (SPI), which is the factoring part of SPI, and searches the structure passed by SPI to find a approximate value. This paper also presents the theoretical background for this approach. Empirical results are presented for three heuristics of this approach and a best first search approach tested in a set of randomly generated belief nets and a net from the real world.
Resource Type	Capstone Project
Date Issued	1991-11-18
Degree Level	Master's
Degree Name	Master of Science (M.S.)
Degree Field	Computer Science
Degree Grantor	Oregon State University
Commencement Year	1991
Advisor	D'Ambrosio, Bruce
Committee Member	Lewis, Ted G. Tadepalli, Prasad
Academic Affiliation	Computer Science
权利声明	In Copyright
Publisher	Oregon State University
Peer Reviewed	No
Language	English [eng]
File Format	pdf

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