本章目录

20 Exact inference for graphical models 707
20.1 Introduction 707
20.2 Belief propagation for trees 707
20.2.1 Serial protocol 707
20.2.2 Parallel protocol 709
20.2.3 Gaussian BP * 710
20.2.4 Other BP variants * 712
20.3 The variable elimination algorithm 714
20.3.1 The generalized distributive law * 717
20.3.2 Computational complexity of VE 717
20.3.3 A weakness of VE 720
20.4 The junction tree algorithm * 720
20.4.1 Creating a junction tree 720
20.4.2 Message passing on a junction tree 722
20.4.3 Computational complexity of JTA 725
20.4.4 JTA generalizations * 726
20.5 Computational intractability of exact inference in the worst case 726
20.5.1 Approximate inference 727

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