Probabilistic Reasoning with Bayesian Networks
Represent a joint distribution over many variables with a graph and a handful of small tables, then answer queries against it exactly when the structure allows and by sampling when it does not.
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Bayesian Networks and the Joint Distribution
25 min · 100 XPA directed acyclic graph with a conditional probability table at each node, the product that defines what it means, and the conditional independences that make it compact.
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Exact Inference: Enumeration and Elimination
30 min · 120 XPAnswer P(Burglary | JohnCalls, MaryCalls) by summing out the hidden variables, see the repeated work, and remove it with factors and variable elimination.
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Approximate Inference by Sampling
30 min · 120 XPDraw events from the network, count them, and see why rejection sampling wastes almost everything, how likelihood weighting keeps every sample, and what Gibbs sampling does instead.
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