MA 483 - Bayesian Data Analysis

  • Credit Hours: 4R-0L-4C
  • Term Available: W (Odd years)
  • Graduate Studies Eligible: Yes
  • Prerequisites: MA 381
  • Corequisites: None

This course offers an introduction to statistical inference under the Bayesian framework in addition to elements of basic study design. Building from Bayes' Rule for probability computations, we develop a framework of estimation, hypothesis testing and prediction. Topics include the construction of prior distributions to quantify a priori beliefs about unknown parameters, modeling available data, and using data to update beliefs about parameters. Applications include inference for a single response, comparing groups, and regression models; modern applications will be covered, time permitting. The course will make use of heavy use of computational tools for Bayesian inference, including Markov Chain Monte Carlo (MCMC) methods.

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