Middlebury

MATH 0412

Bayesian Statistics

Bayesian Statistics
In this course, we will learn about the Bayesian paradigm of statistics, in which one’s inferences about parameters or hypotheses are updated as evidence accumulates. The goals of the course include understanding basic concepts of Bayesian inference; deriving posterior distributions; assessing the adequacy of Bayesian models; and effectively communicating results. Topics covered include one-parameter models, conjugacy, and Gibbs samplers. Real-world data and applications will feature heavily in this course. (MATH 0311) 2.5 hr. lect.
Subject:
Mathematics
Department:
Mathematics & Statistics
Division:
Natural Sciences
Requirements Fulfilled:
DED
Equivalent Courses:
STAT 0412 *

Sections in Fall 2023, School Abroad Japan (Tokyo)