PNS0157, Understanding and Implementing Bayesian Modelling: a Course from Beginnings to Hierarchical Complexity, 5.0 Hp
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Syllabus

Finalized by: Ekologiforskarskolans styrgrupp , 2022-05-05
Valid from :

Level

Third cycle

Grading Scale

The grade requirements within the course grading system are set out in specific criteria. These criteria must be available by the course start at the latest.

Course language

English

Entry Requirements

Objectives

Students will have a grounded understanding of: (1) how Bayes rule is derived from the principles of probability, (2) the advantages of Bayesian analyses and when they should be used, (3) the probability distributions needed for linking their data to ecological models (Gaussian, Poisson, Binomial, Gamma, Beta, Bernoulli, Multinomial), (4) how statistical models are constructed and described, (5) how to use hierarchical modelling to account for structures in the data and to ask specific ecological questions that are unavailable in other modelling approaches, (6) how to interpret and communicate information contained in the model results, and (7) how to use R and JAGS to do all of these things

Content

The students are encouraged to work both independently and within small groups to help problem-solve modelling issues. This encourages the student to understand how to solve problems when working under different working conditions and encourages communication and collaboration. Both teachers circulate around the room offering encouragement, clarifying concepts and helping students solve problems as they arise. Lectures are given around the key concepts that form the basis of the exercises. These key concepts given in lectures are:

1. Bayes rule
2. Probability
3. Likelihood
4. Prior knowledge
5. MCMC
6. General Linear Models
7. Describing Models
8. Using JAGS
9. Making Predictions
10. Interactions, Collinearity and Scaling
11. Non-linear Models
12. Hierarchical Models
13. Adding Prior Information to Models
14. Overdispersion
15. Posterior Predictive Checks (is my model ok?)
16. Adding Observational Uncertainty
17. State-Space (time-dependant) Models

Examination Formats and Requirements for Passing the Course

The course will be structured around the principles of formative assessment. This means that there will be no end-of-course assignment or exam to complete: rather the teachers will continuously assess the students during the course. The advantage of this approach is that students who understand material quickly can be given more challenging exercises to progress with, while students who are having trouble understanding key concepts can be given more time and additional exercises to help them understand before moving on. This ensures all students work to their ability to gain the most out of the course. Thus passing the course will be determined by attendance and work ethic, rather than an arbitrary achievement level.

Responsible Department/Equivalent

Department of Ecology

Supplementary information

Other Information

The course organiser and primary teacher will be Matt Low and the teaching assistant will be Malin Aronsson, both Dept. of Ecology.