Computational Biology Trainings

by András Aszódi

Bayesian Statistics with R

The Bayesian framework represents an alternative approach to statistical modelling. Bayesian analysis describes how to update our initially incomplete "prior" knowledge with experimental observations (the "evidence") to obtain a better understanding (the "posterior") of the phenomenon studied. This concept fits very well the way how scientific research works.

In addition, Bayesian Networks represent an important step towards analysing causal relationships which traditional statistical methods cannot model.

This course teaches you the principles of Bayesian statistics with hands-on practical examples in R.



Online exercises are available when this course is running. Please select the option "Bayes" from the dropdown in the "Request an exercise notebook" form.

Out of scope

We cannot go into the specific data analysis problems of your particular project.

Note that "Bayesian statistics" is a huge subject and this introductory course only teaches you the basics.


Good knowledge of how to calculate probabilities and familiarity with discrete and continuous distributions are required. If you attended our Think Statistics! with R training then you are well prepared.

In addition, some basic familiarity with R is required. Our R as a programming language training provides a good foundation.

Practical information

Number of participants: minimum 5, maximum 10.

Length: The course takes one half-day, from 09:00 to 13:00 with 2 breaks.