Oregon State University 4 credits DS
Introduces main concepts of Bayesian analysis from the statistical foundations to model implementation. Reviews and implements a variety of widely used models from a Bayesian perspective including linear regression, Poisson and Negative Binomial regression, and logistic regression. Emphasizes the computational implementation of these models and discusses numerical approximations for posterior inference, including Markov Chain Monte Carlo approaches.
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