本章目录

7 Linear regression 217
7.1 Introduction 217
7.2 Model specification 217
7.3 Maximum likelihood estimation (least squares) 217
7.3.1 Derivation of the MLE 219
7.3.2 Geometric interpretation 220
7.3.3 Convexity 221
7.4 Robust linear regression * 223
7.5 Ridge regression 225
7.5.1 Basic idea 225
7.5.2 Numerically stable computation * 227
7.5.3 Connection with PCA * 228
7.5.4 Regularization effects of big data 230
7.6 Bayesian linear regression 231
7.6.1 Computing the posterior 232
7.6.2 Computing the posterior predictive 233
7.6.3 Bayesian inference when σ2 is unknown * 234
7.6.4 EB for linear regression (evidence procedure) 238

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