The basics, single and multiparameter models  
What?  When?  Where?  Read?  Contents  Material 
Lecture 1  Oct 27, 1012  VN  BDA, Ch. 12.  Likelihood. Intro to Bayesian inference. Bernoulli model.  Slides 
Lecture 2  Oct 28, 1012  D33  BDA, Ch. 12.  Normal and Poisson models. Conjugate and noninformative priors.  Slides 
Lecture 3  Oct 28, 1315  D33  BDA, Ch. 3.13.6  Multiparameter models, Marginalization, Normal with unknown variance, Multinomial, Multivariate normal  Slides 
Computer 1  Oct 30, 1012  PC45  Gridding posteriors. Basic simulation.  Lab  
Exercise 1  Oct 31, 1012  VN  Exercise 1.1, 1.6, 2.1, 2.5, 2.8, 2.11, 2.14 in BDA  
Regression models  
What?  When?  Where?  Read?  Contents  Material 
Lecture 4  Nov 4, 1012  D28  Prediction and making decisions  Slides  
Lecture 5  Nov 11, 1012  VN  BDA 4.14.2, 14.12 and 14.614.8.  Linear regression. Binary regression. Posterior approximation.  Slides 
Exercise 2  Nov 11, 1517  VN  Exercise 2.11, 4.1, 2.13, 2.19, 2.22 in BDA.  
Computer 2  Nov 12, 1012  PC45  Bayesian polynomial regression with conjugate prior.  Lab JapanTemp.dat 

Lecture 6  Nov 14, 1012  D37  BDA 21.121.2 Chapter 2.12.3 and 2.5 in this book 
Nonlinear regression. Shrinkage priors. Gaussian processes.  Slides 
Tackling more advanced models with MCMC  
What?  When?  Where?  Read?  Contents  Material 
Lecture 7  Nov 19, 1012  VN  BDA Ch. 1011  Gibbs sampling and data augmentation  Slides 
Lecture 8  Nov 21, 1517  D33  BDA Ch. 1011  Markov Chain Monte Carlo, MetropolisHastings  Slides 
Lecture 9  Nov 24, 1012  D33  BDA Ch. 5.15.5.  Hierarchical models  Slides RStan Slides 
Exercise 3  Nov 24, 1517  VN  Exercise 3.1, 3.2, 3.5 in BDA.  
Computer 3  Nov 27, 1012  PC45  Gibbs sampling and binary regression.  Lab CanadianWages.dat 

Model Inference  
What?  When?  Where?  Read?  Contents  Material 
Lecture 10  Dec 1, 1012  G33  Article on Bayes factors  Model inference  
Lecture 11  Dec 1, 1315  VN  Model inference  
Lecture 12  Dec 2, 1012  G33  BDA 20.120.2.  Bayesian variable selection.  
Exercise 4  Dec 3, 1315  VN  Exercise 3.12, 14.3, 14.4, 14.7 in BDA.  
Computer 4  Dec 4, 1012  PC45  The Metropolis algorithm  
Project  
Project deadline  Dec 21, Midnight. 
Note: Linköping University makes use of so called academic quarters.
Exercise x.y means the y:th exercise in Chapter x.BDA = Bayesian Data Analysis book
VN = Von Neumann
TBA = To be announced.
Page responsible: Mattias Villani
Last updated: 20141124