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STAT 340.00 Bayesian Statistics 6 credits

Closed: Size: 20, Registered: 25, Waitlist: 0

CMC 206

Synonym: 61454

Adam Loy

Formerly MATH 315) An introduction to statistical inference and modeling in the Bayesian paradigm. Topics include Bayes’ Theorem, common prior and posterior distributions, hierarchical models, Markov chain Monte Carlo methods (e.g., the Metropolis-Hastings algorithm and Gibbs sampler) and model adequacy and posterior predictive checks. The course uses R extensively for simulations.

Prerequisite: Statistics 250 (formerly Mathematics 275)

Fomerly Mathematics 315

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You must take 6 credits of each of these.
You must take 6 credits of each of these,
except Quantitative Reasoning, which requires 3 courses.
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