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Course Description
| STAT 330 - Bayesian Statistics | ||
| Instructor(s): William Jefferys |
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| Description: This is a course in Bayesian statistics. Bayesian inference is a powerful and increasingly popular statistical approach, which allows one to deal with complex problems in a conceptually simple and unified way. The recent introduction of Markov Chain Monte Carlo (MCMC) simulation methods has made possible the solution of large problems in Bayesian inference that were formerly intractable. This course will introduce the student to the basic methods and techniques of modern Bayesian inference, including parameter estimation, MCMC simulation, hypothesis testing, and model selection/model averaging in the context of practical problems. |
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| Prerequisites: STAT 241 or 251 or instructor permission. |
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| Methodologies: Statistical modeling, Other: Markov chain Monte Carlo (MCMC) |
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| Domains: Not specific to any particular application domain |
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| Frequency: Once a year | ||
| Credits: 3 | ||
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