The basics, single- and multiparameter models | |||||

What? |
When? |
Where? |
Read? |
Contents |
Material |

Lecture 1 | Oct 27, 10-12 | VN | BDA, Ch. 1-2. | Likelihood. Intro to Bayesian inference. Bernoulli model. | Slides |

Lecture 2 | Oct 28, 10-12 | D33 | BDA, Ch. 1-2. | Normal and Poisson models. Conjugate and non-informative priors. | Slides |

Lecture 3 | Oct 28, 13-15 | D33 | BDA, Ch. 3.1-3.6 | Multiparameter models, Marginalization, Normal with unknown variance, Multinomial, Multivariate normal | Slides |

Computer 1 | Oct 30, 10-12 | PC4-5 | Gridding posteriors. Basic simulation. | Lab | |

Exercise 1 | Oct 31, 10-12 | 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, 10-12 | D28 | Prediction and making decisions | ||

Lecture 5 | Nov 6, 10-12 | D33 | BDA 4.1-4.2, 14.1-2 and 14.6-14.8. | Linear regression. Binary regression. Posterior approximation. | |

Lecture 6 | Nov 11, 10-12 | VN | BDA 21.1-21.2 Chapter 2.1-2.3 and 2.5 in this book |
Non-linear regression. Shrinkage priors. Gaussian processes. | |

Exercise 2 | Nov 11, 15-17 | VN | Exercise 2.11, 4.1, 2.13, 2.19, 2.22 in BDA. | ||

Computer 2 | Nov 12, 10-12 | PC4-5 | Bayesian polynomial regression with conjugate prior. | ||

Tackling more advanced models with MCMC | |||||

What? |
When? |
Where? |
Read? |
Contents |
Material |

Lecture 7 | Nov 19, 10-12 | VN | BDA Ch. 10-11 | Gibbs sampling and data augmentation | |

Lecture 8 | Nov 20, 10-12 | VN | BDA Ch. 10-11 | Markov Chain Monte Carlo, Metropolis-Hastings | |

Lecture 9 | Nov 21, 15-17 | D33 | BDA Ch. 5.1-5.5. | Hierarchical models | |

Exercise 3 | Nov 24, 15-17 | VN | Exercise 3.1, 3.2, 3.5 in BDA. | ||

Computer 3 | Nov 27, 10-12 | PC4-5 | Gibbs sampling and binary regression. | ||

Model Inference | |||||

What? |
When? |
Where? |
Read? |
Contents |
Material |

Lecture 10 | Dec 1, 10-12 | G33 | Article on Bayes factors | Model inference | |

Lecture 11 | Dec 1, 13-15 | VN | Model inference | ||

Lecture 12 | Dec 2, 10-12 | G33 | BDA 20.1-20.2. | Bayesian variable selection. | |

Exercise 4 | Dec 3, 13-15 | VN | Exercise 3.12, 14.3, 14.4, 14.7 in BDA. | ||

Computer 4 | Dec 4, 10-12 | PC4-5 | 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: 2014-10-29