This course is an introduction to Bayesian theory and applications. Bayesian methods provide a powerful alternative to classical (frequentist) statistical methods. In this course we emphasize the development of Bayesian inference and conduct hands-on data analysis within the Bayesian framework. We begin with a brief overview of essential distinctions between classical and Bayesian methods and progress through the following topics: conjugate families of distributions; posterior simulation and analysis when the posterior is intractable; Bayesian regression and classification; and hierarchical models.

Units: 1

Max Enrollment: 15

Prerequisites: MATH 205, an introductory statistics course at Wellesley (STAT 160, STAT 218, ECON 103/SOC 190, PSYC 205, BISC 198, POL 299, or QR 150) or a statistical modeling course (QR 260/STAT 260 or STAT 318 or the QAI Summer Course). MATH 220/STAT 220 (may be taken concurrently).

Instructor: Joseph

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Every other year

Semesters Offered this Academic Year: Fall

Notes: