Approximation Methods for Inference and Learning in Belief Networks: Progress and Future Directions
Progress rept. 15 May 1996-14 Nov 1997,
CALIFORNIA UNIV IRVINE DEPT OF INFORMATION AND COMPUTER SCIENCE
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Belief networks also known as Bayesian networks, causal networks, or probabilistic networks represent dependencies between variables and give a concise specification of a joint probability distribution. They enable a general-purpose inference method that can answer a broad class of queries given information that is uncertain or incomplete. In this research project, we have investigated methods and implemented algorithms for efficiently making certain classes of inference in belief networks, and for automatically learning certain classes of belief networks to make more accurate inferences. The progress on this project falls into two related areas inference and learning. In each case, progress has been both on understanding and unifying existing approaches and the development of new methods.