MATH 275 - Probability & Bayesian Statistics I

3 credits
The Bayesian framework for data analysis is carefully examined with an emphasis on model likelihoods and prior and posterior distributions. Simple probability models (binomial and normal distributions) will be examined with an emphasis on marginal and conditional probability and Bayes’ Theorem. Topics include conjugate and non-informative priors; single- and multi-parameter models; and Bayesian computation methods: Markov-chain Monte-Carlo simulation and Gibbs sampler. Frequentist and Bayesian approaches to hypothesis testing and statistical inference are compared. This course is the first of a two-course sequence and requires some coding using appropriate statistical software.

Prerequisite(s): MATH 171 and MATH 175, both with grade of C- or higher
Corequisite(s): None