上传两本非常经典的贝叶斯统计的教材，非扫描版
其一：
《Bayesian statistical modelling》
Chapter 1 Introduction: The Bayesian Method, its Benefits and Implementation
Chapter 2 Bayesian Model Choice, Comparison and Checking
Chapter 3 The Major Densities and their Application
Chapter 4 Normal Linear Regression, General Linear Models and Log-Linear Models
Chapter 5 Hierarchical Priors for Pooling Strength and Overdispersed Regression Modelling
Chapter 6 Discrete Mixture Priors
Chapter 7 Multinomial and Ordinal Regression Models
Chapter 8 Time Series Models
Chapter 9 Modelling Spatial Dependencies
Chapter 10 Nonlinear and Nonparametric Regression
Chapter 11 Multilevel and Panel Data Models
Chapter 12 Latent Variable and Structural Equation Models for Multivariate Data
Chapter 13 Survival and Event History Analysis
Chapter 14 Missing Data Models
Chapter 15 Measurement Error, Seemingly Unrelated Regressions, and Simultaneous Equations

其二：
《The Bayesian Choice - From Decision-Theoretic Foundations to Computational Implementation》
1 Introduction 1
2 Decision-Theoretic Foundations 51
3 From Prior Information to Prior Distributions 105
4 Bayesian Point Estimation 165
5 Tests and Confidence Regions 223
6 Bayesian Calculations 285
7 Model Choice 343
8 Admissibility and Complete Classes 391
9 Invariance, Haar Measures, and Equivariant Estimators 427
10 Hierarchical and Empirical Bayes Extensions 457
11 A Defense of the Bayesian Choice 507