Bayesian Decision Analysis for Fisheries Management |
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| Credit Value: 3 Credits
Schedule: Not offered, 2025/2026 Prerequisites, Restrictions, and Notes: Minimal entry requirement: first-year undergraduate calculus and reasonably good quantitative skills, such as those gained through FISH 504 and FISH 505. Students wishing to take the course should also have good computing skills and be able to use Excel spreadsheets to implement simple time series models, apply Monte Carlo simulation methods, and estimate the parameters of non-linear models. Maximum class size: 15 students. Description: This course provides an introduction to Bayesian data analysis and statistical modelling methods that are commonly utilized in fisheries stock assessment. Methods covered include approaches that have been applied to fisheries stock assessment to formulate priors, grid-based, importance sampling, and Markov Chain Monte Carlo Methods for integration of posterior distributions for fisheries and model parameters, introduction to WinBUGS software for fisheries modelling, diagnostics to assess convergence and goodness of fit, methods to compute Bayes’ posteriors (or factors) for alternative fisheries models, fisheries hierarchical models, and Bayesian mark-recapture methods and state-space population dynamics models for fish stock assessment. Assessment: This course has four graded components, each counting for 25% of the final grade.
Course Instructor: Dr. Murdoch McAllister |
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