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Approximate Inference by Sampling

Draw 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.

IntermediateModule 330 min · 120 XP
A thousand sampled events streaming through the sprinkler network, seven hundred of them fading out as rejected, then the same evidence kept fixed while weights accumulate instead.

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