LFU:online

After completion of this module, students are able to design customised analyses in a Bayesian framework. Students will understand the probabilistic underpinnings of their models and be able to express this both graphically and in code. This course will prepare participants for the data analysis for their thesis.

This course will cover the basics of Bayesian statistical methods with applications in ecology. Bayesian methods are a powerful set of tools that are increasingly used with complex ecological data. These methods can also be extended quite easily beyond conventional analyses to include process-based/mechanistic models. Topics include probability and likelihood, Bayesian software, implementations of various models (e.g., GLMs, hierarchical models) in a Bayesian framework, diagnostics, and statistical inference.

Participation during the course (20%)

Project presentation, individually or in groups (40%)

Project report, individually (40%)

Will be discussed in the first lesson.

successful completion of compulsory module 1.

Participants will need to be comfortable programming in R and have a good understanding of basic statistics.

Students will have the opportunity to analyse data for their thesis in the course and are encouraged to come prepared with data, but this is not required.