Preface
1
Quick start
1.1
Linear regression
1.1.1
L0 criteria
1.1.2
Bayesian model selection
1.2
Logistic regression
1.3
Non-Linear effects via Generalized Additive Models (GAMs)
2
Bayesian model selection and averaging
2.1
A simplest example
2.2
General framework
2.3
Bayesian model averaging
2.4
Prediction problems
2.5
Prior on models
2.5.1
Binomial prior
2.5.2
Beta-Binomial prior
2.5.3
Complexity prior
2.5.4
A simple example
2.6
Prior on coefficients
2.6.1
Local priors
2.6.2
Non-local priors
2.6.3
Sensitivity to prior variance
2.7
Computation
2.7.1
Marginal likelihoods and model-specific posteriors
2.7.2
Model search
2.7.3
MCMC basics
2.8
Exercises
3
L0 criteria
3.1
Basics
3.2
Theoretical considerations
3.3
Model search
3.3.1
Optimization methods
3.3.2
MCMC
3.4
Exercises
4
Generalized linear models
4.1
Approximating the marginal likelihood
4.2
MCMC model search
4.3
Assessing MCMC convergence
5
Generalized additive models
6
Empirical Bayes for transfer learning
7
Survival data
8
Gaussian graphical models
9
Gaussian mixture models
High-dimensional model choice. A hands-on take
8
Gaussian graphical models
To be added.