A Practical Course in Bayesian Modeling
Latest News

[This section contains the latest news with respect to the course. Check this section for last minute changes in logistics, useful hints on homework exercises, and general advice.]

1. Part of the course book, answers to exercises, and computer code are all available at www.bayesmodels.com.
2. Additional lectures are here.
3. The plenary lectures and homework exercises can be found below.
Goals

First, to get hands-on experience with an easy-to-use computer program that allows you to implement all kinds of Bayesian models. Second, to understand why Bayesian statistics is right, and everything else is wrong.
Content

In this course, plenary lectures will provide the theoretical background of Bayesian statistics, whereas practical computer exercises will teach you how to use the WinBUGS/JAGS program and apply it to a wide range of different statistical models. After completing this course, you will have gained not only a new understanding of statistics (yes, it can make sense), but also the technical skills to implement statistical models that professional researchers in the field of psychology dare only dream about.
Your Lecturers

Eric-Jan Wagenmakers, Dora Matzke
Email: EJ.Wagenmakers@gmail.com, d.matzke@uva.nl.
WWW: http://www.ejwagenmakers.com, http://dora.erbe-matzke.com/
The Course Book

Together with Michael Lee, we have written a book on Bayesian cognitive modeling (see www.bayesmodels.com). Michael has already used the book to teach several courses at UCI (the University of California at Irvine), and the book has also been used at OSU, Penn State, Leuven, Washington University, etc. This is the 9th year that the book will be used at the UvA.

Note: Although the book has been published, feedback about typos and unclarities is always appreciated!
Your Grade

After the first class, your homework assignments will feature sets of questions that you have to answer. You will get five sets of questions, and we will grade each on the usual scale from 1-10. Your final grade is the mean of all five sets.

Class 1: Introduction to Bayesian Reasoning
Lecture

The ShinyApp discussed in class is available here, under "A First Lesson in Bayesian Inference". For now, ignore all the stuff that has to do with model selection and Bayes factors -- we are only estimating parameters at this point.

Homework Assignments for Class 2

1. Read chapters 1 and 2 of the course book. NB. We use R so you can skip 2.2.3. Also, if you use JAGS you do not have to execute the steps in 2.2.2.
2. Write down any questions you might have.
3. Do the exercises (chapter 1 only) and check whether you understand the answers.
4. Try to get the example from chapter 2 to work on your computer.
Class 2: More Introduction
Lecture

The lecture will discuss MCMC sampling.

Homework Assignments for Class 3

1. Work through chapter 3 of the course book.
2. Do the exercises from chapter 3 and check whether you understand the answers.
3. Do the exercises from this pdf and bring the answers with you on Wednesday. You will be graded on these ones! [grade I] Please hand in your own work, and do not collaborate in groups. You are free to ask and receive advice on the BB forum, however.
Class 3
Lecture

The lecture will discuss MCMC sampling as well as prior and posterior predictives.

Homework Assignments for Class 4

1. Work through chapter 4 of the course book.
2. Do the exercises from chapter 4 and check whether you understand the answers.
3. Do the exercises from this pdf and bring the answers with you on Monday. You will be graded on these ones! [grade II]
Class 4
Lecture

The lecture discussed the homework assignments. The example of Bob's IQ is here.

Homework Assignments for Class 5

1. Work through chapter 5 of the course book.
2. Do the exercises from chapter 5 and check whether you understand the answers.
3. Do the exercises from this pdf and bring the answers with you this Wednesday. You will be graded on these ones! [grade III]. NB. An explanation and several examples of the zeros and ones tricks are here.

The lecture discussed the homework assignments. The example of Bob's IQ is here.
Class 5
Lecture

The lecture discussed the homework assignments.

Homework Assignments for Class 6

1. Work through chapter 6 of the course book, up to and including section 6.4.
2. Do the exercises from chapter 6 (up to and including section 6.4) and check whether you understand the answers.
3. Do the exercises from this pdf and bring the answers with you on next Monday. You will be graded on these ones! [grade IV]
Class 6
Lecture

The lecture will discuss the homework assignments. A few different methods to create order-restrictions are here (txt file) and here (R file). Please Email me if you discover any others.

Homework Assignments for Class 7

1. Finish working through chapter 6 of the course book.
2. Complete the exercises from chapter 6 and check whether you understand the answers.
3. Do the exercise from this pdf and bring the answer with you this Wednesday. You will be graded on this one! [grade V -- final grade]