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  • 人工智能入门(四):uncertainty&基于统计的学习

    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)

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  • 原文地址:https://www.cnblogs.com/yizhaoAI/p/9944780.html
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