Bayesian Networks(BN) have been considered to be one of the most widely used probabilistic data modelling and propositional uncertainty processing paradigms. They exploit the underlying conditional independences in the domain by providing compact graphical representations for high-dimensional joint distributions. A BN consists of two components - a directed acyclic graph whose nodes correspond to a pre-specified set of attributes or random variables; and a set of conditional probability distributions (CPDs) over the attributes. The techniques that have been developed for learning BNs from data have been shown to be remarkably effective for some data mining problems, especially probabilistic descriptive data mining.

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Bayesian Networks Graphical Models Uncertainty Random Variable Statistical Inference Joint Probability Distribution Marginal Probability Distribution Conditional Probability Distribution Bayes' Rule Directed Acyclic Graph Likelihood Function Junction Tree