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PSTAT215A

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PSTAT 215A - Bayesian Inference

Statistics & Applied Probability College of Letters and Science

Full Course Title

Bayesian Inference

Instructor Name(s)

STAFF

Course Description

Fundamentals of the Bayesian inference, including the likelihood principle, the discrete version of Bayes theorem, prior and posterior distributions, Bayesian point and interval estimations, and predictions. Bayesian computational methods such as Laplacian approximations andMarkov Chain Monte Carlo (MCMC) simulation.

Unit Value

4

Maximum number of times course can be repeated for additional credit

99

Maximum Units

99

Prerequisites

PSTAT 207A or PSTAT 220A (may be taken concurrently).