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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