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CMU CS 10701 - Bayes Nets D-Separation & Inference
School name Carnegie Mellon University
Course Cs 10701- Introduction to Machine Learning
Pages 22

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Slide 1Slide 2Slide 3Slide 4Slide 5Slide 6Slide 7Slide 8Slide 9Slide 10Slide 11Slide 12Slide 13Slide 14Slide 15Slide 16Slide 17Slide 18Slide 19Slide 20Slide 21Slide 22Bayes NetsD-Separation & InferenceSome slides taken from previous 10701 recitationsObserve that the grass is wet. What isthe probability that the Sprinkler was on?Monte Carlo Sampling●What is the probability that the sprinkler was on given that the grass is wet?●Sample C, then S, R, and finally W many times.●Approximate P(W), P(S,W) via counting.Why D-Separation?●Helps us understand the dependencies implied by a graph●Helps us perform inference efficiently......XExample

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CMU CS 10701 - Bayes Nets D-Separation & Inference

School: Carnegie Mellon University
Course: Cs 10701- Introduction to Machine Learning
Pages: 22
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