Module 6: Bayesian Statistics

Dates - Venue - Description - Schedule - Target audience - Exam - IMPORTANT: Incorporation in DTP and reimbursement by DS
Course prerequisites - Teacher - Course material - Fees - Enrol


CHANGE IN DATES: Thursday January 16 and 23, February 3, 6 and 13, 2020, from 5.30 pm to 9 pm.
The class scheduled on Thursday January 30 has been moved to Monday February 3.
Please note: The deadline for UGent PhD students who want a refund to open a dossier on the DS website (Application for Recognition) is December 13, 2019


Faculty of Science, Site Sterre, Krijgslaan 281, Building S9, Ghent, lecture room 3.2 (=V3) and pc room 3.1 (=Konrad Zuse), 3rd floor.


Recent years have seen a tremendous increase in the use and development of Bayesian methods for academic research. In its wake, more and more companies employing statisticians are valuing the knowledge brought by these approaches. The goal of this course is to give participants a brief and intensive introduction to Bayesian statistics.

Participants will learn how Bayesian inference differs from classical inference and how to interpret its results in a meaningful way. They will acquire the skills to use Bayesian techniques correctly in a range of practical applications.

Topics that will be discussed include the difference between Bayesian and frequentist/classical probability, the likelihood function, choice of prior distributions, conjugate priors, the posterior distribution and methods for summarizing the posterior. In addition, an overview will be given about the most important Markov Chain Monte Carlo Methods that are often used to simulate the posterior distribution. These methods include the Gibbs sampler, Importance sampling, Metropolis-Hastings and the Slice sampler.

Depending on the interest and background of the participants, the Bayesian estimation of one (or more) of the following approaches will be explained and discussed: linear regression, choice models (logit, probit, multinomial), Bayesian hypothesis testing, quantile regression, mixed models, Bayesian variable selection, …

All exercises in this course will use R together with the rjags R-package and the JAGS software. Note that JAGS is very similar (if not identical) to the popular BUGS/winBUGS language for Bayesian modeling.

Target audience

This course targets professionals and investigators from diverse areas who wish to get acquainted with Bayesian techniques to be able to apply them to their practical applications.


Participants can, if they wish, take part in an exam. Upon succeeding in this test a certificate from Ghent University will be issued.

Please note: For UGent PhD students it is no longer necessary to participate/succeed in this exam to be able to incorporate the course in the DTP.

Incorporation in DTP and reimbursement from DS for UGent PhD students

As a UGent PhD student, to be able to incorporate this course in your Doctoral Training Program (DTP) and get a reimbursement of the registration fee from your Doctoral School (DS) you need to follow strict rules: please take the necessary action in time. The deadline to open a dossier on the DS website (Application for Recognition) for this course is December 13, 2019. Please note that opening a dossier does not mean that you are enrolled. You still need to enrol via the registration form on this site.

Course prerequisites

Participants are expected to have an active knowledge of the basic principles underlying statistical strategies, at a level equivalent to the “Introductory Statistics” course of this program.

Basic knowledge of the statistical programming language R is required.


Prof. dr. Dries Benoit is professor of Business Analytics at the faculty of Economics and Business Administration of Ghent University. He teaches Bayesian statistics to the Business Engineering students and in the Master of Statistical Data-Analysis.

His research is focused on the use of Bayesian statistics for business analytics. He worked on problems ranging from customer relationship management, over credit scoring to pricing and revenue management. Methodologically, he contributed to the field of Bayesian quantile regression and Bayesian robustness.

Course material

• Handouts of slides.

• Recommended books (optional):

  • Book 1: Albert, J. (2007). Bayesian Computation with R, Springer, New York (USA), ISBN 978–0387922973. The present course is largely based on this book.
  • Book 2: Kruschke, J.K. (2011). Doing Bayesian Data Analysis, Elsevier, Oxford (UK), ISBN 978–0123814852. This book discusses R in more depth but stays very accessible.
  • Book 3 – Bernardo J.M. And Smith, A.F.M. (2002). Bayesian Theory, Wiley, New York (USA), ISBN 978–0471494645.


Different prices apply, depending on your main type of employment.

EmploymentModule 6Book 1Book 2Book 3Exam
Industry/Private sector1 1.080 65 70 80 30
Non-profit, government, university outside AUGent2 445 65 70 80 30
(Doctoral) student outside AUGent2 315 65 70 80 30

1 If three or more employees from the same company enrol simultaneously for this course a reduction of 10% on the module price is taken into account.

2 AUGent staff and AUGent doctoral students who pay through use of an SAP internal order/invoice can participate at these special rates.

Enrol for this course