Bayesian core: a practical approach to computational Bayesian statistics (study group)

Scope

4 weekly exercises (1 for everybody plus 3 exercise of your own choice) are needed to collect credits.

Type

Advanced studies

Prerequisites

The minimal prerequisites for this course are a mastering of basic probability theory for discrete and continuous variables and of basic statistics (MLE, sufficient statistics).

Lectures

Room C131 on Thursdays 10-12 during period IV (on May 3 in room C130).

Content scheme

The purpose of this book is to provide a self-contained entry to practical & computational Bayesian statistics using generic examples from the most common models. The emphasis on practice is a strong feature of this book in that its primary audience is made of graduate students that need to use (Bayesian) statistics as a tool to analyze their experiments and/or data sets. The book should also appeal to scientists in all fields, given the versatility of the Bayesian tools. It can also be used for a more classical statistics audience when aiming at teaching a quick entry to Bayesian statistics at the end of an undergraduate program for instance.

The chapters of the book and their topics are:

  1. Normal models
  2. Regression and variable selection
  3. Generalised linear models
  4. Capture--recapture experiments
  5. Mixture models
  6. Dynamic models
  7. Image analysis

Bibliography