Bayesian statistics, simulation and software (2018)

Welcome

This course contains both theoretical and practical aspects of Bayesian inference, and it is important you bring a labtop with relevant software installed as descibed under the Preparation-tab.

The course is organized as 12 half day modules over 6 days (21-23 Nov. and 26-28 Nov. at Fredrik Bajers Vej 7E room 3-109) with the morning module 8:45-12:00 and the afternoon module 12:30-15:45. Under the Plan-tab you will find a course schedule showing the organisation of the topics.

In order to obtain credit for the course, you will need to actively participate in the course (by being present at least 5 out of 6 days) as well as hand-in an exercise after the completion of the course. This exercise will be available towards the end of the course. You are welcome to ask questions during the last module which is devoted to this exercise. The hand-in date is December 12, 2018, and please send your solution to rubak@math.aau.dk

Please bring paper and pencil for notes and classroom exercises since there will be both theoretical exercises and computer experiments. As this is an intensive course and each day only consists of 6.5 hours of lectures and exercises, it is expected that you also work on the material on your own. Except for the exercises towards the end of the course solutions are provided, but please give the exercises a serious try before you turn to the solutions.

Note that the hand-in exercise may be difficult to solve if you haven't actively participated in the course and in particular worked on the exercises.

The course does not follow any particular text book, but rather the course slides and a few notes (referred to in the plan) will be the main course material. However, a few pointers to relevant literature can be found in the following.

Two good introductory level books:

• Peter D. Hoff (2009). A First Course in Bayesian Statistical Methods. Springer. Available through Aalborg University Library here.
• Peter M. Lee (2004). Bayesian Statistics: an introduction, 3rd ed. Arnold.

Additional useful textbooks:

• Andrew Gelman, et al. (2014). Bayesian Data Analysis, 3rd ed. CRC Press.
• Jean-Michel Marin and Christian Robert (2007) Bayesian Core: A Practical Approach to Computational Bayesian Statistics. Springer. Available through Aalborg University Library here
• Jean-Michel Marin and Christian Robert (2014) Bayesian Essentials with R. Springer. Available through Aalborg University Library here
• Olle Häggström (2002). Finite Markov Chains and Algorithmic Applications. Cambridge University Press.
• Christian P. Robert and George Casella (2004). Monte Carlo Statistical Methods, 2nd ed. Springer.
• Maria Rizzo (2007). Statistical Computing with R. Chapman & Hall/CRC.
• Ioannis Ntzoufras (2009). Bayesian Modeling Using WinBUGS. Wiley.

The course lecturers: Jesper Møller (jm@math.aau.dk) and Ege Rubak (rubak@math.aau.dk).