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PNS0157
Understanding and Implementing Bayesian Modelling: a Course from Beginnings to Hierarchical Complexity
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
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
Syllabus and other information
Syllabus
PNS0157 Understanding and Implementing Bayesian Modelling: a Course from Beginnings to Hierarchical Complexity, 5.0 Credits
Subjects
BiologyEducation cycle
Postgraduate levelGrading scale
Pass / Failed
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
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 thingsContent
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) ModelsAdditional information
The course organiser and primary teacher will be Matt Low and the teaching assistant will be Malin Aronsson, both Dept. of Ecology.Responsible department
Department of Ecology