Statistical Machine Learning Methods for Bioinformatics VII ...

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Bayesian Networks • Directed graphical models (also called Belief Networks) • Popular with AI and statistics communities. • A model with both directed and undirected arcs is called a chain graph
Comparison of Directed and Undirected Graphical Models • Independence relationship of directed graph is more complicated. • A -> B can encode causal relationship • Directed models can encode deterministic relationship, and are easier to learn (fit to data).
Page 1 and 2: Statistical Machine Learning Method
Page 3 and 4: Definition of Graphical Model • P
Page 5: A C B A ┴ B | C
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 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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