Textbook in PDF format

Preface
Introduction
Bayesian Modelling
Statistical Model
Bayes Model
Advantages
Sequential Analysis
Big Data
Hierarchical Models
List of Problems
Choice of Prior
Subjective Priors
Conjugate Priors
Non-informative Priors
Laplace Prior
Jeffreys Prior
Reference Priors
List of Problems
Decision Theory
Basics of Decision Theory
Bayesian Decision Theory
Common Bayes Decision Rules
Quadratic Loss
Absolute Error Loss
Prediction
The 0–1 Loss
Intrinsic Losses
The Minimax Criterion
Bridges
List of Problems
Asymptotic Theory
Consistency
Schwartz’ Theorem
List of Problems
Normal Linear Models
Univariate Linear Models
Bayes Linear Models
Conjugate Prior: Parameter θ = β, σ2 Known
Conjugate Prior: Parameter θ = (β, σ2)
Jeffreys Prior
Linear Mixed Models
Bayes Linear Mixed Model, Marginal Model
Bayes Hierarchical Linear Mixed Model
Multivariate Linear Models
Bayes Multivariate Linear Models
Conjugate Prior
Jeffreys Prior
List of Problems
Estimation
Maximum a Posteriori (MAP) Estimator
Regularized Estimators
Bayes Rules
Estimation in Univariate Linear Models
Estimation in Multivariate Linear Models
Credible Sets
Credible Sets in Linear Models
Prediction
Prediction in Linear Models
List of Problems
Testing and Model Comparison
Bayes Rule
Bayes Factor
Point Null Hypothesis
Bayes Factor in Linear Model
Improper Prior
Bayes Information
Bayesian Information Criterion (BIC)
Deviance Information Criterion (DIC)
List of Problems
Computational Techniques
Deterministic Methods
Brute-Force
Laplace Approximation
Independent Monte Carlo Methods
Importance Sampling (IS)
Sampling from the Posterior
Sampling Importance Resampling (SIR)
Rejection Algorithm
Markov Chain Monte Carlo (MCMC)
Metropolis–Hastings Algorithms
Gibbs Sampling
Approximative Bayesian Computation (ABC)
Variational Inference (VI)
List of Problems
Solutions
Appendix
Discrete Distributions
Continuous Distributions
Multivariate Distributions
Matrix-Variate Distributions
Bibliography
Index