Efficient approximations for the marginal likelihood of incomplete data given a Bayesian network
作者： David Maxwell Chickering , David Heckerman
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摘要: We examine asymptotic approximations for the marginal likelihood of incomplete data given a Bayesian network. consider Laplace approximation and less accurate but more efficient BIC/MDL approximation. also proposed by Draper (1993) Cheeseman Stutz (1995). These are as BIC/MDL, their accuracy has not been studied in any depth. compare these under assumption that is most accurate. In experiments using synthetic generated from discrete naive-Bayes models having hidden root node, we find (1) measure least accurate, bias favor simple models, (2) CS measures complex respectively.
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