Introduction to Bayesian Inference in Practice

Online live sessions from Monday, April 4th to Friday, April 8th, from 12:00 to 16:00 (Madrid time zone).

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, etc.

Bayesian methods have become standard practice in several fields, (e.g. phylogenetic inference, evolutionary (paleo)biology, genomics), yet understanding how these Bayesian machinery works are 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 instructor 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 touch upon hypothesis testing using Bayes factors and Bayesian variable selection.

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 script. 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.

Check the complete program here.

Basic knowledge of Python or R and Statistics. All participants must have a personal computer (Windows, Macintosh), with webcam if possible, anda good internet connection.

[email protected]

English

Online live sessions from Monday to Friday 13:00 to 17:00 (Madrid time zone).

The rest of the time will be taught with recorded classes and assignments, to be done between the live sessions.

32.5 hours: 20 hours of online live lessons, plus 12.5 hours of assignments.

This course is equivalent to 2 ECTS (European Credit Transfer System) at the Life Science Zurich Graduate School.

The recognition of ECTS by other institutions depends on each university or school.

Places are limited to18 participants and will be occupied by strict registration order.

Participants who have completed the course will receive a certificate at the end of it.

Instructors

Dr. Daniele Silvestro

University of Gothenburg

Sweden

Tobias Andermann

University of Gothenburg

Sweden