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732A46 Bayesian Learning

Timetable


What? When? Where? Read? Contents Material
Lecture 1 Oct 28, 10-12 DK BDA, Ch. 1-2. Likelihood. Intro to Bayesian inference. Bernoulli model. Slides 1/page
Slides 4/page
Lecture 2 Oct 28, 13-15 VN BDA, Ch. 1-2. Normal and Poisson models. Conjugate and non-informative priors. Slides 1/page
Slides 4/page
Lecture 3 Oct 29, 10-12 AT BDA, Ch. 3.1-3.3 and 3.5-3.6 Multiparameter models, Marginalization, Normal with unknown variance, Multinomial, Multivariate normal Slides 1/page
Slides 4/page
Exercise 1 Oct 29, 13-15 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, 10-12 PC4-5 Gridding posteriors. Basic simulation. Lab 1
Lecture 4 Nov 6, 13-15 VN Prediction and making decisions Slides 1/page
Slides 4/page
Lecture 5 Nov 7, 10-12 VN BDA 4.1-4.2, 14.1-3 and 14.6-7. Linear regression. Binary regression. Posterior approximation. Slides 1/page
Slides 4/page
Exercise 2 Nov 7, 13-15 VN 2.11, 4.1, 2.13, 2.19, 2.22 in BDA. Solutions
Computer 2 Nov 11, 10-12 PC4-5 Bayesian polynomial regression with conjugate prior. Lab 2
JapanTemp.dat
Lecture 6 Nov 11, 13-15 VN Splines. Shrinkage priors. Variable selection. Slides 1/page
Slides 4/page
Lecture 7 Nov 12, 10-12 AT Chapter 2.1-2.3 and 2.5 in this book Gaussian processes Slides 1/page
Slides 4/page
Exercise 3 Nov 12, 13-15 CB 3.1, 3.2, 3.5 in BDA. Solutions
Lecture 8 Nov 20, 10-12 VN BDA Ch. 10-11 Gibbs sampling and data augmentation Slides 1/page
Slides 4/page
Lecture 9 Nov 20, 13-15 VN BDA Ch. 10-11 Markov Chain Monte Carlo, Metropolis-Hastings Slides 1/page
Slides 4/page
Lecture 10 Nov 21, 10-12 VN RStan session Slides
example code
Computer 3 Nov 21, 13-15 Statistik PUL Gibbs sampling and binary regression. Lab 3
CanadianWages.dat
Exercise 4 Dec 4, 10-12 VN 3.12, 14.3, 14.4, 14.7 in BDA.
Computer 4 Dec 4, 13-15 PC4-5 The Metropolis algorithm Lab 4 - new version
dbetaLogit.R
Lecture 11 Dec 5, 10-12 VN Article on Bayes factors Model inference Slides 1/page
Slides 4/page
Lecture 12 Dec 5, 13-15 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 book
VN = Von Neumann
AT = Alan Turing
DK = Donald Knuth
CB = Charles Babbage
TBA = To be announced.

Page responsible: Mattias Villani
Last updated: 2014-02-06