Statistical Machine Learning Methods for Bioinformatics VII ...

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Advantages of BN • Compact & intuitive representation • Captures causal relationships • Efficient model learning (parameters and structure) • Deals with noisy data • Integration of prior knowledge • Effective inference algorithms N. Friedman, 2005
Conditional Probability Distribution • Discrete variable: CPT, conditional probability table C P(S=F) P(S=T) F 0.5 0.5 T 0.9 0.1 P(C=F) P(C=T) 0.5 0.5 Cloudy C P(R=F) P(R=T) F 0.8 0.2 T 0.2 0.8 Sprinklet Rain WetGrass S R P(W=F) P(W=T) F F 1.0 0.0 T F 0.1 0.9 F T 0.1 0.9 T T 0.01 0.99
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: Comparison of Directed and Undirect
Page 11 and 12: Compact Representation of Joint Pro
Page 13 and 14: P(W=1) is a normalizing constant, e
Page 15 and 16: Top-Down and Bottom-Up Reasoning
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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