
Tractable Bayesian Learning of Tree Belief Networks
In this paper we present decomposable priors, a family of priors over st...
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EDML: A Method for Learning Parameters in Bayesian Networks
We propose a method called EDML for learning MAP parameters in binary Ba...
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Beyond Uniform Priors in Bayesian Network Structure Learning
Bayesian network structure learning is often performed in a Bayesian set...
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A Bayesian Network Scoring Metric That Is Based On Globally Uniform Parameter Priors
We introduce a new Bayesian network (BN) scoring metric called the Globa...
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Gaussian Process Networks
In this paper we address the problem of learning the structure of a Baye...
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Bayes factors with (overly) informative priors
Priors in which a large number of parameters are specified to be indepen...
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On Sensitivity of the MAP Bayesian Network Structure to the Equivalent Sample Size Parameter
BDeu marginal likelihood score is a popular model selection criterion fo...
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Likelihoods and Parameter Priors for Bayesian Networks
We develop simple methods for constructing likelihoods and parameter priors for learning about the parameters and structure of a Bayesian network. In particular, we introduce several assumptions that permit the construction of likelihoods and parameter priors for a large number of Bayesiannetwork structures from a small set of assessments. The most notable assumption is that of likelihood equivalence, which says that data can not help to discriminate network structures that encode the same assertions of conditional independence. We describe the constructions that follow from these assumptions, and also present a method for directly computing the marginal likelihood of a random sample with no missing observations. Also, we show how these assumptions lead to a general framework for characterizing parameter priors of multivariate distributions.
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