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In this lecture, we explore how Bayesian Belief Networks (BBNs) are trained under different scenarios, including observable variables, hidden variables, known network structures, and unknown structures. We discuss learning Conditional Probability Tables (CPTs), gradient-based optimization, Expectation-Maximization (EM), and plate notation for ...
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