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Introduction to the Bayesian paradigm. Markov Chain Monte Carlo estimation using WinBUGS. Comparison with frequentist statistics. Noninformative and improper priors. Inference and model selection. Linear and generalized linear models. Hierarchical Bayes.
Note(s): Access to a Windows PC and an approved statistics package is required for analysis of data.
Note: You may enrol in a postgraduate course (that is a 700-, 800- or 900-level course) if you meet the prerequisites for that course and have been admitted to a qualification which lists the course in its schedule.
|2019||Semester One full semester||Distance|
|2019||Semester One full semester||Internal||Manawatu Campus|
|2020||Semester One full semester||Distance|
|2020||Semester One full semester||Internal||Manawatu Campus|
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