Detailed information about the course

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Introduction to Bayesian Inference in Practice


12 - 16 April 2021


Prof. Daniele Silvestro, University of Fribourg


Prof. Daniele Silvestro, University of Fribourg

Tobias Andermann, University of Gothenburg (SE)


Most researchers in life sciences are exposed in their research to a multitude of methods and algorithms to test hypotheses, infer parameters, explore empirical data sets. Bayesian methods have become standard practice in several fields (e.g., phylogenetic inference, evolutionary biology, genomics), yet understanding how these Bayesian machinery works is not always trivial.

This course is based on the assumption that the easiest way to understand the principles of Bayesian inference and the functioning of the main algorithms is to implement these methods yourself.

The instructors will outline the relevant concepts and basic theory, but the focus of the course will be to learn how to do Bayesian inference in practice. He will show how to implement the most common algorithms to estimate parameters based on posterior probabilities, such as Markov Chain Monte Carlo samplers, and how to build hierarchical models.

He will also discuss hypothesis testing, Bayesian variable selection, and Bayesian applications in machine learning.

Rather than demonstrating existing libraries or software, the course will take a learn-by-doing approach, in which participants will implement their own MCMCs using R or Python (templates for both languages will be provided).

After completion of the course, the participants will have gained a better understanding of how the main Bayesian methods implemented in many programs used in biological research work. Participants will also learn how to model at least basic problems using Bayesian statistics and how to implement the necessary algorithms to solve them.

Participants are expected to have some knowledge of R or Python (each can choose their preferred language), but they will be guided "line-by-line" in writing their scripts. The aim is that, by the end of the week, each participant will have written their own MCMC – from scratch! Participants are encouraged to bring own datasets and questions and we will (try to) figure them out during the course and implement scripts to analyze them in a Bayesian framework.


More information will follow


University of Fribourg, or online depending on the sanitary situation


Full attendance and active participation.

Make sure to sign the attendance list each and every day!


Basic knowledge of Python or R and Statistics. All participants must bring their personal computer.

12 to 16 April 2021

University of Fribourg, or online, depending on the sanitary situation
Room TBD

Marta Bellone




PhD students of the DPEE are eligible for the reimbursement of incurred travel expenses by train (half-fare card, and 2nd class).

The online reimbursement system is now in place, so once the course is over you will be able to request the reimbursement via MyCUSO, without sending the paperwork to the coordinator.


Please contact the coordinator of the doctoral program (ecologie-evolution(at) BEFORE the beginning of the course. NO reimbursement of accommodation without the coordinator of the doctoral program agreement prior of the course. In case of overnight stay, please post the original tickets and original bills along with the reimbursement form to:

Marta Bellone
Doctoral Program in Ecology and Evolution
Department of Biology
University of Fribourg
Chemin du Musée 10
CH-1700 Fribourg

NO reimbursement of meal expenses


Register via your MyCUSO account!!


Deadline for registration: 28 March 2021

Priority is given to PhD students of the DPEE until 15 March 2021. After this deadline, first comes, first serves

Registration Fees:

Free for participants enrolled in the CUSO Ecology & Evolution doctoral program

In case of cancellations, before the deadline: free

Late cancellations or no-show: 100 CHF administrative fee

Other participants: please contact the program coordinator at ecologie-evolution(at)



Deadline for registration 28.03.2021
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