Structure and Parameter Learning for Causal Independence and Causal Interaction Models
نویسندگان
چکیده
We begin by discussing causal independence models and generalize these models to causal interaction models Causal interaction mod els are models that have independent mech anisms where mechanisms can have several causes In addition to introducing several particular types of causal interaction mod els we show how we can apply the Bayesian approach to learning causal interaction mod els obtaining approximate posterior distribu tions for the models and obtain MAP and ML estimates for the parameters We illus trate the approach with a simulation study of learning model posteriors
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تاریخ انتشار 1997