Bayesian statistics offer flexible techniques for researchers who cannot properly analyze their data using methods based on classical statistics. This course will provide a sound basis in Bayesian statistics for those who want to:
• understand what Bayesian statistics is about;
• use Bayesian statistics to build and evaluate statistical models;
• get hands on experience with Bayesian statistics in Openbugs, R, JAGS, and Bain.
This 4 day course zooms in on the key concepts of Bayesian Statistics and advanced techniques for data-analysis. Topics that are covered include: Bayes’ theorem, Gibbs sampling, the Metropolis-Hastings algorithm, the Bayes factor, the evaluation of informative hypotheses, Bayesian methods for linear regression, moderation, and mediation, and data-analysis in OpenBUGS or R and JAGS.
The course is aimed at researchers who not only work with statistical tools, but are also interested in the development and evaluation of statistical tools. Among these are psychometricians, sociometricians, epidemiologists, and statisticians. The only requirement is familiarity with the following concepts: the likelihood function, the p-value, analysis of variance, and multiple regression.
Note: Participants need to bring a laptop computer to the course, with Bain and OpenBugs [http://www.openbugs.net], or alternatively with R ((https://www.r-project.org/) installed and JAGS installed (https://sourceforge.net/projects/mcmc-jags/files/JAGS/4.x/). The R + JAGS option is recommended especially for Mac users.
Among our Methodology and Statistics postgraduate courses, there are two other courses that address Bayesian statistics. The distinction between these three courses is as follows:
- Theory-Based Hypothesis Evaluation Using the p-value, Bayes factor, and information criteria, an applied course on evaluating theory-based hypotheses (via Bayesian and information-theoretic model selection) and on addressing causes of the replication crisis. This course focusses on the application of model/hypothesis selection methods.
- Applied Bayesian Statistics, to learn more about Bayes’ theorem, Gibbs sampling and the Metropolis-Hastings algorithm, Bayes factors, the evaluation of informative hypotheses, and Bayesian methods for linear regression, moderation, and mediation with observed variables. This course pays attention to the statistical theory using formulas.
- A gentle introduction to Bayesian Statistics, for material on Bayesian Structural Equation Modeling. This course focusses on SEM and does not cover hypothesis testing nor Bayes factors.
For an overview of all our summer school courses offered by the Department of Methodology and Statistics please click here.
Prof. dr. Herbert Hoijtink, dr. Ellen Hamaker, dr. Milica Miočević
Tuition fee for PhD candidates from the Faculty of Social and Behavioural Sciences from Utrecht University will be funded by the Graduate School of Social and Behavioural Sciences.
Irma Reyersen - firstname.lastname@example.org