Probabilistic graphical models are a powerful framework for representing complex distributions over random variables through graphs. These models capture the dependencies and relationships between variables, making it easier to reason about joint and conditional probabilities. By utilizing nodes to represent random variables and edges to denote relationships, these models can simplify computations in probability theory and enable effective inference.
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