Bayesian model selection and statistical modeling
Tomohiro Ando

This website provides R code for several worked examples that appear in the book Bayesian model selection and statistical modeling.

Chapter 1

Chapter1 Section1.1 Analysis of Nikkei 225 index data   Nikkei225 index Data

Chapter 2

Chapter2 Section2.8.1 Subset variable selection

Chapter2 Section2.8.2 Smoothing parameter selection

Chapter 3

Chapter3 Section3.1.3 Asymptotic normality of the posterior mode: Comparison of the constructed density function of the posterior mode

Chapter3 Section3.2.2 Bayesian central limit theorem: Poisson distribution with conjugate prior

Chapter3 Section3.3.3 Laplace method: Bernoulli distribution with a uniform prior

Chapter 4

Chapter4 Section4.2.4 Gibbs sampling for seemingly unrelated regression model

Chapter4 Section4.6.1 A direct Monte Carlo algorithm for linear regression model

Chapter 5

Chapter5 Section5.2.3 Bayes factor: Poisson models with conjugate priors

Chapter5 Section5.3.1 Marginal likelihood: Bernoulli distribution with conjugate prior

Chapter5 Section5.5.1 Evaluation of the approximation error of BIC

Chapter5 Section5.5.2 Link function selection for binomial regression with BIC

Chapter5 Section5.5.4 Survival analysis with BIC

Chapter5 Section5.6.1 Nonlinear regression models using basis expansion predictors with GBIC

Chapter5 Section5.6.2 Nonlinear multiclass classification using basis expansion predictors with GBIC

Chapter5 Section5.8.3 Bayesian spatial modeling

Chapter5 Section5.9.3 Bayesian sensitivity analysis of Value at Risk

Chapter 6

Chapter6 Section6.1.1 Bayesian analysis of multinomial probit models

Chapter6 Section6.2.1 Bayesian analysis of the ordered probit model   Credit Rating Data

Chapter6 Section6.3.1 The marginal likelihood evaluations for seemingly unrelated regression model

Chapter6 Section6.7.1 Bayesian analysis of the probit model   Default Data

Chapter 7

Chapter7 Section7.2.1 Hierarchical Bayesian modeling for logistic regression

Chapter7 Section7.4.1 P-spline regression model with Gaussian noise

Chapter7 Section7.4.2 P-spline logistic regression modeling with MBIC

Chapter7 Section7.5.2 Nonlinear multiclass classification using basis expansion predictors with GIC

R information and packages

R The R Project for Statistical Computing.

R is free from R-Project for Statistical Computing.

An Introduction to R The Official Introduction.