本章目录
10 Directed graphical models (Bayes nets) 307
10.1 Introduction 307
10.1.1 Chain rule 307
10.1.2 Conditional independence 308
10.1.3 Graphical models 308
10.1.4 Graph terminology 309
10.1.5 Directed graphical models 310
10.2 Examples 311
10.2.1 Naive Bayes classifiers 311
10.2.2 Markov and hidden Markov models 312
10.2.3 Medical diagnosis 313
10.2.4 Genetic linkage analysis * 315
10.2.5 Directed Gaussian graphical models * 318
10.3 Inference 319
10.4 Learning 320
10.4.1 Plate notation 320
10.4.2 Learning from complete data 322
10.4.3 Learning with missing and/or latent variables 323
10.5 Conditional independence properties of DGMs 324
10.5.1 d-separation and the Bayes Ball algorithm (global Markov
properties) 324
10.5.2 Other Markov properties of DGMs 327
10.5.3 Markov blanket and full conditionals 327
10.6 Influence (decision) diagrams * 328
github下载链接 ：https://github.com/916718212/Machine-Learning-A-Probabilistic-Perspective-.git