Statistical Machine Learning Methods for Bioinformatics VII ...

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Explaining Away • S and R are the two causes competing to explain the observed data. • So if w is not observed, S and R are marginally independent. • If w is observed, S and R become conditionally dependent. P(S=1|W=1, R=1) = 0.1945 < P(S=1|W=1) • This is called “explaining away”. In statistics, it is known as Berkson’s paradox, or “selection bias”.
Top-Down and Bottom-Up Reasoning • Bottom up: In the water sprinkler example, we had evidence of an effect (wet grass), and inferred the most likely cause. • Top down: We can compute the probability that the grass will be wet given that it is cloudy. (how causes generate effects).
Page 1 and 2: Statistical Machine Learning Method
Page 3 and 4: Definition of Graphical Model • P
Page 5 and 6: A C B A ┴ B | C
Page 7 and 8: Comparison of Directed and Undirect
Page 9 and 10: Conditional Probability Distributio
Page 11 and 12: Compact Representation of Joint Pro
Page 13: P(W=1) is a normalizing constant, e
Page 17 and 18: In the first column, when we have t
Page 19 and 20: Example A B A B C C D Is A independ
Page 21 and 22: Dynamic Bayesian Networks • DBNs
Page 24 and 25: Linear Dynamic Systems (LDSs) and K
Page 27 and 28: Efficient Inference Algorithms •
Page 29 and 30: • The principle of distributing s
Page 31 and 32: Local Message Passing • If the BN
Page 33 and 34: Learning of BN • The graph topolo
Page 35 and 36: Known Structure, Partial Observabil
Page 37 and 38: General Application Examples • Mi
Page 39 and 40: Discovery of Regulatory Mechanism /
Page 41 and 42: Gene Bayesian Network Gene E Qualit
Page 43 and 44: Solutions • Sparse candidate algo
Page 45: Important References: BN in Bioinfo

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