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Bayesian networks are annotated directed graphs that encode probabilistic relations among variables in uncertain-reasoning problems. The variables may be discrete or continuous. Bayesian networks are usually formed through cause and effect frameworks and are suitable for computing all probabilities of interest since they determine joint probability distributions for domains.