Theoretical Statistics
Bayesian networks are graphical models that represent the probabilistic relationships among a set of variables using directed acyclic graphs (DAGs). Each node in the graph represents a variable, while the edges between nodes signify conditional dependencies, allowing for the modeling of complex joint distributions. This structure makes Bayesian networks especially useful in reasoning about uncertainty and making predictions based on observed data, deeply connected to the concept of conditional probability.
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