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Syllabus

PNG0084 Understanding & Implementing Bayesian Ecological Modelling: a Course from Beginnings to Hierarchical Complexity, 5.0 Credits

Subjects

Biology

Education cycle

Postgraduate level

Grading scale

Prior knowledge

Admitted to PhD studies. The course will assume no prior knowledge of Bayesian statistics but will require that students are able to use R (e.g. creating sequences, lists, importing data) and have a basic understanding of R programming principles (e.g. loops, indexing etc).

Objectives

Objective, including learning outcomes 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 ncourages 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.

Additional information

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