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. / Graduation date: 2002
| Identifer | oai:union.ndltd.org:ORGSU/oai:ir.library.oregonstate.edu:1957/28769 |
| Date | 21 June 2001 |
| Creators | Wang, Haiou |
| Contributors | D'Ambrosio, Bruce D. |
| Source Sets | Oregon State University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
Page generated in 0.0018 seconds