STAT 041. Theory for Bayesian Inference


We will review common probability distributions and their relationships, including distributions derived from the Normal distribution and from the Poisson process. These distributions form the building blocks for probability modeling. Bayesian inference makes use of distributions to describe prior beliefs about unknown parameters. We will discuss conjugate and non-informative prior distributions, along with methods for evaluating and summarizing posterior distributions. These will include sequential conditional simulation, rejection sampling and Markov Chain Monte Carlo.
Prerequisite: STAT 061  or permission of the instructor.
Natural science and engineering.
Writing course.
1 credit.
Eligible for GLBL-Core
Fall 2022. Everson.
Catalog chapter: Mathematics and Statistics  
Department website: http://www.swarthmore.edu/mathematics-statistics


Check the Spring 2022 Schedule of Courses

Check the Fall 2022 Schedule of Courses




Print-Friendly Page (opens a new window)