Corpus ID: 1011569

Learning Bayesian Networks: Search Methods and Experimental Results

  title={Learning Bayesian Networks: Search Methods and Experimental Results},
  author={M. Chickering and D. Geiger and D. Heckerman},
We discuss Bayesian approaches for learning Bayesian networks from data. First, we review a metric for computing the relative posterior probability of a network structure given data developed by Heckerman et al. (1994a,b,c). We see that the metric has a property useful for inferring causation from data. Next, we describe search methods for identifying network structures with high posterior probabilities. We describe polynomial algorithms for nding the highestscoring network structures in the… Expand
Learning Bayesian networks: The combination of knowledge and statistical data
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Learning Bayesian Networks: The Combination of Knowledge and Statistical Data
A methodology for assessing informative priors needed for learning Bayesian networks from a combination of prior knowledge and statistical data is developed and how to compute the relative posterior probabilities of network structures given data is shown. Expand
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  • Computer Science, Mathematics
  • Innovations in Bayesian Networks
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Bayesian networks are frequently used to model statistical dependencies in data. Without prior knowledge of dependencies in the data, the structure of a Bayesian network is learned from the data.Expand