Statistical Machine Learning Methods for Bioinformatics VII ...

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Variable Elimination: Water Sprinkler Network
• The principle of distributing sums over products can be generalized greatly to apply to any commutative summing. This forms the basis of many common algorithms, such as Viterbi decoding and the Fast Fourier Transform. • The amount of work we perform when computing a marginal is bounded by the size of the largest term that we encounter. Choosing a summation (elimination) ordering to minimize this is NP-hard, although greedy algorithms work well in practice.
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 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: Efficient Inference Algorithms •
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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