STATS 731 - Bayesian Inference
Faculty
Science
Department
Statistics
Points:
15
Available Semesters:
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Course Components
Labs
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Workshops
Description: A course in practical Bayesian statistical inference covering: the Bayesian approach specification of prior distributions, decision-theoretic foundations, the likelihood principle, asymptotic approximations, simulation methods, Markov Chain Monte Carlo methods, the BUGS and CODA software, model assessment, hierarchical models, application in data analysis.
Prerequisites / Restrictions
Prerequisite: STATS 331 and 15 points from STATS 210, 225
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