Event Description
In this workshop, plenary lectures provide the theoretical background of Bayesian inference, and practical computer exercises teach you how to apply the popular JAGS software to a wide range of different statistical models. After completing this workshop, you will have gained not only a new understanding of statistics, but also the technical skills to implement models that are appropriate for the substantive hypotheses that you seek to test.
Objectives
Our first objective is for you to experience first-hand how a Bayesian approach to data analysis can improve inference and increase understanding. Our second objective is for you to obtain practical experience with JAGS, easy-to-use computer programs that allow the user to implement all kinds of Bayesian models.
Target audience
This workshop is meant for researchers who want to learn how to apply Bayesian inference in practice. Most applications we discuss are taken from the field of cognitive science. The workshop is based on the book Bayesian Cognitive Modeling: A practical course written by Michael Lee and Eric-Jan Wagenmakers. As the workshop covers a variety of topics within cognitive science and exercises of varying difficulty, the course material is appropriate for researchers with a wide range of prior knowledge and interests.
Although some basic knowledge of Bayesian inference is an advantage, this is not a prerequisite. In the course we use JAGS in combination with R or Matlab (the choice is yours), and therefore some basic knowledge of either R or Matlab is also an advantage.
Participation requires that you bring your own laptop. Please make sure to have JAGS and R/Matlab installed and running on your laptop before the first day. You can find information on how to install JAGS here.
You can contact us at bayescourse@gmail.com.
Theory and Practice of Bayesian Hypothesis Testing: A JASP Workshop
Our workshop Theory and Practice of Bayesian Hypothesis Testing: A JASP Workshop takes place in Amsterdam and online. You can still sign up for this workshop!
Call For Posters
Participants are invited to present a poster introducing their research topic. The poster sessions will be held on Monday (July 10) between 17.00-18.00 at the workshop venue. If you would like to participate in the poster session, you can indicate this during registration.
Information for students
We offer a special discount for students and award a certificate of participation indicating the total number of hours spent on the workshop. Students may inquire with their institution about earning equivalent European Credits (ECs) towards their degree. Participation in this workshop is worth 3 ECs (84 h). In order to be eligible for these ECs, students should prepare for the workshop by reading the following background literature:
Show literature list
- Chapter 1 of Lee, M. D., & Wagenmakers, E.-J. (2014). Bayesian Cognitive Modeling. Cambridge University Press. https://doi.org/10.1017/cbo9781139087759 (available for free here)
- Etz, A., & Vandekerckhove, J. (2017). Introduction to Bayesian Inference for Psychology. Psychonomic Bulletin & Review, 25(1), 5–34. https://doi.org/10.3758/s13423-017-1262-3
- van Ravenzwaaij, D., Cassey, P., & Brown, S. D. (2016). A simple introduction to Markov Chain Monte–Carlo sampling. Psychonomic Bulletin & Review, 25(1), 143–154. https://doi.org/10.3758/s13423-016-1015-8
- Wagenmakers, E.-J., Lodewyckx, T., Kuriyal, H., & Grasman, R. (2010). Bayesian hypothesis testing for psychologists: A tutorial on the Savage–Dickey method. In Cognitive Psychology, 60(3), 158–189). https://doi.org/10.1016/j.cogpsych.2009.12.001
- Lee, M. D., & Vanpaemel, W. (2017). Determining informative priors for cognitive models. Psychonomic Bulletin & Review, 25(1), 114–127. https://doi.org/10.3758/s13423-017-1238-3
- Vanpaemel, W., & Lee, M. D. (2012). Using priors to formalize theory: Optimal attention and the generalized context model. Psychonomic Bulletin & Review, 19(6), 1047–1056. https://doi.org/10.3758/s13423-012-0300-4
- Lee, M. D. (2011). How cognitive modeling can benefit from hierarchical Bayesian models. Journal of Mathematical Psychology, 55(1), 1–7. https://doi.org/10.1016/j.jmp.2010.08.013
Workshop Location
Room 1.01 (Building M)
Roeterseilandcampus
Plantage Muidergracht 12
Amsterdam (NL)
