
Learning Gaussian Networks
We describe algorithms for learning Bayesian networks from a combination...
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A Bayesian Approach to Learning Bayesian Networks with Local Structure
Recently several researchers have investigated techniques for using data...
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Optimizing regularized Cholesky score for orderbased learning of Bayesian networks
Bayesian networks are a class of popular graphical models that encode ca...
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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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On the Use of Skeletons when Learning in Bayesian Networks
In this paper, we present a heuristic operator which aims at simultaneou...
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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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Evaluating structure learning algorithms with a balanced scoring function
Several structure learning algorithms have been proposed towards discove...
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Learning Bayesian Networks: The Combination of Knowledge and Statistical Data
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. Our contributions are threefold. First, we identify two important properties of metrics, which we call event equivalence and parameter modularity. These properties have been mostly ignored, but when combined, greatly simplify the encoding of a user's prior knowledge. In particular, a user can express her knowledgefor the most partas a single prior Bayesian network for the domain. Second, we describe local search and annealing algorithms to be used in conjunction with scoring metrics. In the special case where each node has at most one parent, we show that heuristic search can be replaced with a polynomial algorithm to identify the networks with the highest score. Third, we describe a methodology for evaluating Bayesiannetwork learning algorithms. We apply this approach to a comparison of metrics and search procedures.
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