
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data
We describe algorithms for learning Bayesian networks from a combination...
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A Multivariate Discretization Method for Learning Bayesian Networks from Mixed Data
In this paper we address the problem of discretization in the context of...
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Finding Optimal Bayesian Networks
In this paper, we derive optimality results for greedy Bayesiannetwork ...
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Renormalized Normalized Maximum Likelihood and ThreePart Code Criteria For Learning Gaussian Networks
Score based learning (SBL) is a promising approach for learning Bayesian...
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SparsityBoost: A New Scoring Function for Learning Bayesian Network Structure
We give a new consistent scoring function for structure learning of Baye...
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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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A Bayesian Method Reexamined
This paper examines the "K2" network scoring metric of Cooper and Hersko...
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Learning Gaussian Networks
We describe algorithms for learning Bayesian networks from a combination of user knowledge and statistical data. The algorithms have two components: a scoring metric and a search procedure. The scoring metric takes a network structure, statistical data, and a user's prior knowledge, and returns a score proportional to the posterior probability of the network structure given the data. The search procedure generates networks for evaluation by the scoring metric. Previous work has concentrated on metrics for domains containing only discrete variables, under the assumption that data represents a multinomial sample. In this paper, we extend this work, developing scoring metrics for domains containing all continuous variables or a mixture of discrete and continuous variables, under the assumption that continuous data is sampled from a multivariate normal distribution. Our work extends traditional statistical approaches for identifying vanishing regression coefficients in that we identify two important assumptions, called event equivalence and parameter modularity, that when combined allow the construction of prior distributions for multivariate normal parameters from a single prior Bayesian network specified by a user.
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