Learning2 - Learning Structure and Parameters The principle structure Sh as a discrete RV Treat network Calculate structure posterior Integrate

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10/25/10 Learning Structure and Parameters The principle Treat network structure, Sh, as a discrete RV Calculate structure posterior Integrate over uncertainty in structure to predict The practice Computing marginal likelihood, p(D|Sh), can be difficult. Learning structure can be impractical due to the large number of hypotheses (more than exponential in # of nodes)
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10/25/10 source: www.bayesnets.com
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10/25/10 Approach to Structure Learning § model selection find a good model, and treat it as the correct model § selective model averaging select a manageable number of candidate models and pretend that these models are exhaustive Experimentally, both of these approaches produce good results. i.e., good generalization
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10/25/10
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10/25/10 SLIDES STOLEN FROM DAVID HECKERMAN
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10/25/10
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10/25/10 Interpretation of Marginal Likelihood Using chain rule for probabilities Maximizing marginal likelihood also maximizes sequential prediction ability! Relation to leave-one-out cross validation
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This note was uploaded on 10/24/2010 for the course CSCI 4202 at Colorado.

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Learning2 - Learning Structure and Parameters The principle structure Sh as a discrete RV Treat network Calculate structure posterior Integrate

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