本章目录

17 Markov and hidden Markov models 589
17.1 Introduction 589
17.2 Markov models 589
17.2.1 Transition matrix 589
17.2.2 Application: Language modeling 591
17.2.3 Stationary distribution of a Markov chain * 596
17.2.4 Application: Google’s PageRank algorithm for web page ranking * 600
17.3 Hidden Markov models 603
17.3.1 Applications of HMMs 604
17.4 Inference in HMMs 606
17.4.1 Types of inference problems for temporal models 606
17.4.2 The forwards algorithm 609
17.4.3 The forwards-backwards algorithm 610
17.4.4 The Viterbi algorithm 612
17.4.5 Forwards filtering, backwards sampling 616
17.5 Learning for HMMs 617
17.5.1 Training with fully observed data 617
17.5.2 EM for HMMs (the Baum-Welch algorithm) 618
17.5.3 Bayesian methods for “fitting” HMMs * 620
17.5.4 Discriminative training 620
17.5.5 Model selection 621
17.6 Generalizations of HMMs 621
17.6.1 Variable duration (semi-Markov) HMMs 622
17.6.2 Hierarchical HMMs 624
17.6.3 Input-output HMMs 625
17.6.4 Auto-regressive and buried HMMs 626
17.6.5 Factorial HMM 627
17.6.6 Coupled HMM and the influence model 628
17.6.7 Dynamic Bayesian networks (DBNs) 628

github下载链接：https://github.com/916718212/Machine-Learning-A-Probabilistic-Perspective-.git