Bayesian Statistics

Course Syllabus

Instructor: Sewon Park

Email: swpark0413@sookmyung.ac.kr

Semester: Spring / 2026

Class Time & Location: Monday & Wednesdays, 15:00–16:15, Room B116, Changhak B

Office Hours: Wednesdays, 14:00-14:50 or by appointment

Course Description

This course covers the fundamental theories and practical implementation methods of Bayesian statistics, introducing key concepts such as Bayes’ theorem, prior and posterior distributions, Markov chain Monte Carlo (MCMC), and Bayesian model selection. Along with theoretical understanding, students will develop the ability to apply Bayesian methods to real-world problems through data analysis exercises using Python.

Learning Objectives

By the end of the course, students will be able to:

• Understand the principles of Bayes’ theorem and explain the roles of prior and posterior distributions.
• Design Bayesian models, select appropriate prior distributions, and perform posterior inference.
• Understand computational techniques such as MCMC and use them to estimate complex posterior distributions.
• Implement Bayesian analysis using Python and apply it to real-world datasets.

Prerequisites

• Mathematical Statistics I, II
• Linear Algebra
• Linear Regression

Course Materials

• Textbook:
  이재용 & 이기재, 베이즈 데이터 분석, 한국방송통신대학교출판문화원.
• Reference:
  • Peter D. Hoff, A first Course in Bayesian Statistical Methods, Springer.
  • Andrew Gelman, Bayesian Data Analysis, Chapman & Hall/CRC Texts in Statistical Science.

Grading Policy

Weekly Schedule