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Most biologists 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, comparative methods, population genetics/genomics), yet understanding how this 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. I 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. I will show how to implement the most common algorithms to estimate parameters based on posterior probabilities, such as Markov Chain Monte Carlo and Gibbs samplers, and how to build hierarchical models. We 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). 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.
Course teachers are: Daniele Silvestro and Bengt Oxelman.
The University of Gothenburg course plan can be found here.
Application deadline is September 30, 2017. Registration here.
A 2 ECTS course certificate will be given to students that pass the course by ForBio.
Fee: No course fee. ForBio members that are graduate students or postdocs in Norway can have travel and accommodation expenses covered. Lunches are provided.
Contact Hugo de Boer (hugo.deboer@nhm.uio.no) for more information.