Last modified: 28 Jun 2018 10:27
This course is one of the few postgraduate courses in Europe to provide an introduction to Bayesian inference for biologists, which is increasingly used in advanced quantitative research. A combination of lectures and personal research will provide you with the core concepts necessary to understand recent research in your field and apply Bayesian approaches to your own research. Hands-on computer tutorials will also allow you to implement statistical models in a Bayesian context and provide you with the essential skills for taking it further.
|Session||First Sub Session||Credit Points||7.5 credits (3.75 ECTS credits)|
|Campus||Old Aberdeen||Sustained Study||No|
Week 1: Introduction to Bayesian statistics. After a refresher in probability theory and linear modelling, students are introduced to Bayes theorem, Bayesian inference, and estimation tools. Week 2-3: Bayesian implementation of models for various study designs. Students will learn to implement statistical models in the R/BUGS language and fit them to ecological data. Students will gain experience in the visualisation and validation of models and focus on their ecological interpretation. Students will start by implementing relatively simple models that they have already covered using a frequentist approach in previous statistics courses (BI5009 and BI5010) and progress to models suited to more advanced study designs.
Information on contact teaching time is available from the course guide.
1 online assessment via myaberdeen and 1 short essay.
There are no assessments for this course.
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