Complexity of probabilistic inference in belief nets--an experimental study

Graduate Thesis Or Dissertation

Public Deposited

Citeable URL: https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/tx31qn32z

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Attribute Name	Values
Creator	Li, Zhaoyu
Abstract	There are three families of exact methods used for probabilistic inference in belief nets. It is necessary to compare them and analyze the advantages and the disadvantages of each algorithm, and know the time cost of making inferences in a given belief network. This paper discusses the factors that influence the computation time of each algorithm, presents the predictive model of the time complexity for each algorithm and shows the statistical results of testing the algorithms with randomly generated belief networks.
Resource Type	Masters Thesis
Date Available	2013-04-02T20:58:52+00:00
Date Issued	1990-11-16
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
Academic Affiliation	Computer Science
Non-Academic Affiliation	Oregon State University. Graduate School
Subject	Artificial intelligence Probabilities Algorithms
Rights Statement	Copyright Not Evaluated
Publisher	Oregon State University
Peer Reviewed	No
Language	English [eng]
Digitization Specifications	File scanned at 300 ppi (Monochrome) using Capture Perfect 3.0 on a Canon DR-9050C in PDF format. CVista PdfCompressor 4.0 was used for pdf compression and textual OCR.
Replaces	http://hdl.handle.net/1957/37963

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