Logic sampling, likelihood weighting and AIS-BN : an exploration of importance sampling Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/gm80hz86h

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  • Logic Sampling, Likelihood Weighting and AIS-BN are three variants of stochastic sampling, one class of approximate inference for Bayesian networks. We summarize the ideas underlying each algorithm and the relationship among them. The results from a set of empirical experiments comparing Logic Sampling, Likelihood Weighting and AIS-BN are presented. We also test the impact of each of the proposed heuristics and learning method separately and in combination in order to give a deeper look into AIS-BN, and see how the heuristics and learning method contribute to the power of the algorithm. Key words: belief network, probability inference, Logic Sampling, Likelihood Weighting, Importance Sampling, Adaptive Importance Sampling Algorithm for Evidential Reasoning in Large Bayesian Networks(AIS-BN), Mean Percentage Error (MPE), Mean Square Error (MSE), Convergence Rate, heuristic, learning method.
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  • File scanned at 300 ppi (Monochrome) using Capture Perfect 3.0.82 on a Canon DR-9080C in PDF format. CVista PdfCompressor 4.0 was used for pdf compression and textual OCR.
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  • description.provenance : Made available in DSpace on 2012-04-17T17:15:53Z (GMT). No. of bitstreams: 1 WangHaiou2002.pdf: 229044 bytes, checksum: 3b3aaaa9c40bdbfb62e779ca035c77a1 (MD5) Previous issue date: 2001-06-21
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  • description.provenance : Approved for entry into archive by Patricia Black(patricia.black@oregonstate.edu) on 2012-04-17T17:15:53Z (GMT) No. of bitstreams: 1 WangHaiou2002.pdf: 229044 bytes, checksum: 3b3aaaa9c40bdbfb62e779ca035c77a1 (MD5)

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