What?  When?  Where?  Read?  Contents  Material 
Lecture 1  Oct 28, 1012  DK  BDA, Ch. 12.  Likelihood. Intro to Bayesian inference. Bernoulli model.  Slides 1/page Slides 4/page 
Lecture 2  Oct 28, 1315  VN  BDA, Ch. 12.  Normal and Poisson models. Conjugate and noninformative priors.  Slides 1/page Slides 4/page 
Lecture 3  Oct 29, 1012  AT  BDA, Ch. 3.13.3 and 3.53.6  Multiparameter models, Marginalization, Normal with unknown variance, Multinomial, Multivariate normal  Slides 1/page Slides 4/page 
Exercise 1  Oct 29, 1315  VN  Exercise 1.1, 1.6, 2.1, 2.5, 2.8, 2.11, 2.14 in BDA 
Photocopies of the exercises Solutions More solutions  scans in Swedish. 

Computer 1  Nov 6, 1012  PC45  Gridding posteriors. Basic simulation.  Lab 1  
Lecture 4  Nov 6, 1315  VN  Prediction and making decisions  Slides 1/page Slides 4/page 

Lecture 5  Nov 7, 1012  VN  BDA 4.14.2, 14.13 and 14.67.  Linear regression. Binary regression. Posterior approximation.  Slides 1/page Slides 4/page 
Exercise 2  Nov 7, 1315  VN  2.11, 4.1, 2.13, 2.19, 2.22 in BDA.  Solutions  
Computer 2  Nov 11, 1012  PC45  Bayesian polynomial regression with conjugate prior.  Lab 2 JapanTemp.dat 

Lecture 6  Nov 11, 1315  VN  Splines. Shrinkage priors. Variable selection.  Slides 1/page Slides 4/page 

Lecture 7  Nov 12, 1012  AT  Chapter 2.12.3 and 2.5 in this book  Gaussian processes  Slides 1/page Slides 4/page 
Exercise 3  Nov 12, 1315  CB  3.1, 3.2, 3.5 in BDA.  Solutions  
Lecture 8  Nov 20, 1012  VN  BDA Ch. 1011  Gibbs sampling and data augmentation  Slides 1/page Slides 4/page 
Lecture 9  Nov 20, 1315  VN  BDA Ch. 1011  Markov Chain Monte Carlo, MetropolisHastings  Slides 1/page Slides 4/page 
Lecture 10  Nov 21, 1012  VN  RStan session  Slides example code 

Computer 3  Nov 21, 1315  Statistik PUL  Gibbs sampling and binary regression.  Lab 3 CanadianWages.dat 

Exercise 4  Dec 4, 1012  VN  3.12, 14.3, 14.4, 14.7 in BDA.  
Computer 4  Dec 4, 1315  PC45  The Metropolis algorithm  Lab 4  new version dbetaLogit.R 

Lecture 11  Dec 5, 1012  VN  Article on Bayes factors  Model inference  Slides 1/page Slides 4/page 
Lecture 12  Dec 5, 1315  VN  Bayesian variable selection and Summary  Slides 1/page Slides 4/page 

Project deadline  Dec 22 
Note: Linköping University makes use of so called academic quarters.
BDA = Bayesian Data Analysis bookVN = Von Neumann
AT = Alan Turing
DK = Donald Knuth
CB = Charles Babbage
TBA = To be announced.
Page responsible: Mattias Villani
Last updated: 20140206