UIBK, Winter Semester 2025-2026
Instructors: Lauren Talluto, Adrienne Étard
Meeting location: Praktikumsraum Biologie (5th Floor)
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 l ikelihood, Bayesian software, implementations of various models (e.g., GLMs, hierarchical models) in a Bayesian framework, diagnostics, and statistical inference.
| Date | Time | Topics | Lecture Notes | Exercises |
|---|---|---|---|---|
| Monday 02.02.26 |
9:00-12:00 |
Intro, Probability review Distributions |
Probability & Distributions (Lauren) |
|
| 13:00-17:00 |
Maximum likelihood Optimisation Markov-chain Monte Carlo |
Maximum Likelihood Estimation (Adrienne) MCMC & Sampling (Lauren) |
||
| Tuesday 03.02.26 |
9:00-12:00 |
Inference I: Sampling & hypothesis tests |
Inference I (Lauren) |
|
| 13:00-17:00 |
Generalised linear models |
(Adrienne) | ||
| Wednesday 04.02.26 |
9:00-12:00 |
Inference II: Priors & Diagnostics Bayesian workflow |
(Lauren) | Project conception Exercises catch-up |
| 13:00-17:00 | Project work | |||
| Thursday 05.02.26 |
9:00-12:00 | Hierarchical & Multilevel Models |
(Adrienne) | Project work |
| 13:00-17:00 |
Inference III: Model selection Multimodel inference |
Model selection (Lauren) |
||
| Friday 06.02.26 |
9:00-12:00 | Advanced Models |
Advanced Models (Lauren) |
Project work |
| 13:00-17:00 | ||||
| Monday 09.02.26 |
9:00-12:00 | Presentations | ||
| 13:00-17:00 | Q&A, Wrap-up (if needed) |
Most lectures will include exercises, which can be completed individually or in groups. We encourage you to work through them as much as you can. We will not grade the exercises, and it is not necessary to turn them in, but we are happy to provide individual feedback/help as needed. During official meeting times, we will also walk through the exercises (as much as time allows) and explain the code to everyone at once.
All of the files for the course are on Github. To create a local copy on your computer, you can follow the instructions here.
Everyone will complete a data analysis group project using a Bayesian analysis of your choice. I am happy to provide feedback to your group as you are developing your project to help steer you toward the proper analysis. Projects will be presented on the last day of instruction. Presentations should be roughly 10-15 minutes, and should include a brief description of the data and the scientific questions, an explanation of the model structure and why the structure is appropriate, other technical details that will help the other groups understand your model, and the status (MCMC diagnostics, results, etc).
Additionally, you should collectively prepare a short write-up of your project, to be submitted by the end of the semester. This write up is your chance to practise concise writing about statistics. Please include an extended abstract introducing the topic (including biological details sufficient to understand the problem) and presenting the study objectives (both in a biological and statistical sense), a methods section that includes a brief discussion of the dataset and a complete description of your model, and a combined results/discussion section that presents your model output (especially as related to your study objectives!) and interprets it. It is not necessary to solve a major biological problem here, but you should be able to draw some biological conclusions about your study system from the output of your model.
These write-ups are due on 28.02.2026. Please note that this is a hard deadline (so that we can submit final marks)!