Bayesian Methods for Ecological and Environmental Modelling

Short course description:

This interactive 5-day course will be a mixture of presentations and practical exercises. It will give you a solid grounding in Bayesian methods that you can use with any kind of model and data set to compare models, estimate parameters, analyse uncertainties and communicate results.

The course will use examples of models and data from natural environment research. However, you will be able to transfer the methods and techniques to any other model, data set or research question. There will be time during practical exercises where you can opt to use the materials provided by the trainers, or use your own model(s) and data.

Learning objectives:

By the end of the course, learners will be able to:

• Understand scientific papers that use Bayesian methods and terminology
• Know how to use Bayesian software
• Design and apply simple Bayesian statistical models
• Compare the relative plausibility of different models
• Comprehensively analyse uncertainties associated with model outputs

Target audience:

• PhD students
• Early career researchers
• Academics/ researchers, including those from industry or the charitable sector

Level:

Intermediate

Learners need to have some practical knowledge of the programming language R. No previous exposure to probability theory or Bayesian methods is necessary, but students may want to read some introductory material beforehand.