Bayesian Statistics (online)

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 Bayesian Statistics

Thursday 26 - Friday 27 November 2020


Nowadays, with the advance of computing and Markov Chain Monte Carlo (MCMC) algorithms, Bayesian statistics is becoming a powerful alternative for traditional Frequentistic statistics. The philosophy behind Bayesian Statistics is discussed. Practical examples are studied, and analysed using the (freeware) program WinBUGS. Participants will be surprised how easy they can tackle problems that are quite complicated to handle with traditional Frequentistic statistics.


null-hypothesis.pngThere are two branches of statistics: Frequentistic statistics and Bayesian statistics. These branches involve different concepts of probability: probability as a "frequency in the long run" or as a "degree of belief". "Degree of belief" comprises the concept of a prior distribution. Bayesians combine two sources of information: prior information, summarized in a prior distribution, and data, represented by a model and associated likelihood. The idea of a likelihood function and maximum likelihood estimation in Frequentistic statistics is briefly introduced (or refreshed). The two sources of information in Bayesian statistics are combined with a theorem of Thomas Bayes. This theorem forms the basis of Bayesian statistics. Possibly, Bayes would have been quite surprised about the consequences of his paper in statistics.

We briefly introduce (and probably refresh):

  • The concept of conditional probability
  • Bayes' theorem
  • An example with Haemophilia. This example, that fits into the framework of Frequentistic statistics as well, has the advantage that implementation of prior information is straightforward. It offers a first impression how prior information and data are combined.
  • Combining the prior information (prior distribution) and the information in the data (likelihood) into a posterior distribution. The posterior distribution summarises all we know about a parameter or parameters based on the prior knowledge and the data.
  • Posterior inference = deriving conclusions from the posterior distribution.
  • Informative priors, non-informative priors, improper priors, conjugate priors.
  • Posterior inference with MCMC, Gibbs sampling and the WinBUGS package.
  • Examples with different models from different areas of application.
General information
Target Group The course is aimed at PhD candidates and other academics
Group Size 24 participants
Course duration 2 days
Language of instruction English
Frequency of recurrence Once a year (Autumn)
Number of credits 0.6 ECTS
Lecturers Dr. Gerrit Gort, Dr. Bas Engel
Prior knowledge Basic Statistics
Location Online (Microsoft Teams)


Fees 1
PE&RC / WIMEK / WASS / EPS / VLAG / WIAS PhD candidates with an approved TSP € 95,- € 145,-
a) All other PhD candidates
b) Postdocs and staff of the above mentioned Graduate Schools
€ 230,- € 280,-
All others € 325,- € 375,-

1 The course fee includes a reader, coffee/tea, and lunches. It does not include accommodation
2 The Early-Bird Fee applies to anyone who REGISTERS ON OR BEFORE 29 OCTOBER 2020


  • If you need an invoice to complete your payment, please send an email to, including ALL relevant details that should be mentioned on the invoice (e.g., purchase order no., specific addresses, attendees, etc.).
  • The Early-Bird policy is such that the moment of REGISTRATION (and not payment) is leading for determining the fee that applies to you.
  • Please make sure that your payment is arranged within two weeks after your registration.
  • It is the participant's responsibility to make sure that he/she (or his/her secretary) completes the payment correctly and in time.
PE&RC Cancellation Conditions
  • Up to 4 (four) weeks prior to the start of the course, cancellation is free of charge.
  • Up to 2 (two) weeks prior to the start of the course, a fee of € 95,- will be charged.
  • In case of cancellation within two weeks prior to the start of the course, a fee of € 230,- will be charged.
  • If you do not show at all, a fee of € 325,- will nevertheless be charged.

Note: If you would like to cancel your registration, ALWAYS inform us (and do note that you will be kept to the cancellation conditions)

More information

Dr. Gerrit Gort
Phone: +31 (0) 317 483570

Dr. Claudius van de Vijver (PE&RC)
Phone: +31 (0) 317 485116


To register, please enter your details below and click "Register".