Bayesian Networks are graphical models that represent a set of variables and their conditional dependencies through a directed acyclic graph (DAG). Each node in the graph represents a random variable, while the edges signify probabilistic dependencies between these variables. These networks are particularly useful for reasoning under uncertainty, as they allow for the incorporation of prior knowledge and the updating of beliefs with new evidence using Bayes' theorem. The joint probability distribution of the variables can be expressed as:
where represents the parent nodes of in the network. Bayesian Networks facilitate various applications, including decision support systems, diagnostics, and causal inference, by enabling efficient computation of marginal and conditional probabilities.
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