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人工智慧入門(四):uncertainty&基於統計的學習

1.belief networks (indenpendence, collider,conditioning / marginalization,connection graph,independence in belief networks,D-separation,uncertain and unreliable evidence)Belief and Markov Networks

2.inference, general inference(variable elimination,bucket elimination algorithm), message passing idea(sum-product algorithm,`belief propagation' or `dynamic programming',max-product algorithm,loop-cut conditioning) 

for singly connected graphs: sum-product, max-product;

for multiply connected graphs: loop-cut conditioning, bucket elimination;

3.MAP,ML,(KL Divergence),Naive Bayes Classi er,Using a Beta prior

4.dealing with miss variables: Missing Completely at random (MCAR), Missing at random(

MAR),Missing NOT at random (MNAR),Expectation Maximisation(EM algorithm)

5.sampling(univariate,rejection,multi-variate,ancestral, Gibbs, importance, sequential importance,particle filter)

6.dynamical models(HMM(filtering, smoothing,prediction),Viterbi, Kalman, particle Filtering (bootstrap filtering)