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Maximum entropy and least square error minimizing procedures for estimating missing conditional probabilities in Bayesian networks

2008
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Computational Statistics & Data Analysis
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Conditional probability tables (CPT) in many Bayesian networks often contain missing values. The problem of missing values in CPT is a very common problem and occurs due to the lack of data on certain scenarios that are observed in the real world but are missing in the training data. The current approaches of addressing the problem of missing values in CPT are very restrictive in that they assume certain probability distributions for estimating missing values. Recently, maximum entropy (ME)

doi:10.1016/j.csda.2007.11.013
fatcat:v6fnerwzg5fqppx7lrp7oqhnnu