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Title of article :
Learning parameters of Bayesian networks from incomplete data via importance sampling Original Research Article
Author/Authors :
Carsten Riggelsen، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2006
Pages :
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Abstract :
We present an algorithm for learning parameters of Bayesian networks from incomplete data. By using importance sampling we are able to assign a score to imputation proposals depending on the quality of such a proposal in combination with the observed data. This in effect makes it possible to approximate the posterior parameter distribution given incomplete data by using a mixture distribution with a tractable number of components. The technique allows for different imputation methods, in particular we propose an imputation method that combines Gibbs sampling and a data augmentation derivative. We evaluate our algorithm, and we compare the results to those obtained with WinBUGS and the EM algorithm.
Keywords :
Bayesian networks , Parameter learning , incomplete data , MCMC , Bayesian statistics
Journal title :
International Journal of Approximate Reasoning
Serial Year :
Link To Document :