MATH 941 (01) - Bayesian and Computational Statistics

Bayesian Statistics

Durham   Engineering&Physical Sciences :: Mathematics&Statistics
Credits: 3.0
Term: Fall 2021 - Full Term (08/30/2021 - 12/13/2021)
Grade Mode: Letter Grading
Class Size:   30  
CRN: 16140
Current approaches to Bayesian modeling and data analysis and related statistical methodology based on computational simulation. Fundamentals of Bayesian estimation and hypothesis testing. Multi-level and hierarchical Bayesian modeling for correlated data. Introduction to Markov chain Monte Carlo based estimation approaches such as the Gibbs sampler and the Metropolis-Hastings algorithm. Mastery of intermediate statistics is required for this course, including: distributions, discrete and continuous random variables, transformation of variables (calculus based), bivariate and multivariate normal distribution, maximum likelihood estimation; working knowledge of linear regression and analysis of variance; basic linear algebra: vectors and matrices, linear spaces, matrix multiplication, inverse of a matrix, positive definiteness. Matrix-vector notation for linear regression and ANOVA.
Department Approval Required. Contact Academic Department for permission then register through Webcat.
Instructors: STAFF

Times & Locations

Start Date End Date Days Time Location
8/30/2021 12/13/2021 MW 11:10am - 12:30pm HORT 201