Introduction to Bayesian Inference in Practice - Transmitting Science course
 

Important dates:

  • Early bird registration31 January 2018

INTRODUCTION TO BAYESIAN INFERENCE IN PRACTICE

 
 

June 11th-15th, 2018, Barcelona (Spain)

Course overview

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 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 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 encouraged to think of potential applications of Bayesian inference in their research, which we will discuss and try to implement during the course.

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