本章目录

3 Generative models for discrete data 65
3.1 Introduction 65
3.2 Bayesian concept learning 65
3.2.1 Likelihood 67
3.2.2 Prior 67
3.2.3 Posterior 68
3.2.4 Posterior predictive distribution 71
3.2.5 A more complex prior 72
3.3 The beta-binomial model 72
3.3.1 Likelihood 73
3.3.2 Prior 74
3.3.3 Posterior 75
3.3.4 Posterior predictive distribution 77
3.4 The Dirichlet-multinomial model 78
3.4.1 Likelihood 79
3.4.2 Prior 79
3.4.3 Posterior 79
3.4.4 Posterior predictive 81
3.5 Naive Bayes classifiers 82
3.5.1 Model fitting 83
3.5.2 Using the model for prediction 85
3.5.3 The log-sum-exp trick 86
3.5.4 Feature selection using mutual information 86
3.5.5 Classifying documents using bag of words 87

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