Bayesian Methods and Gaussian Processes (DATASCI 226)

Bayesian Methods and Gaussian Processes
(DATASCI 226)

Winter 2026 (2-3 units)

This course provides an introduction to Bayesian statistics, Markov Chain Monte Carlo (MCMC) sampling, and Gaussian Processes. The first two units cover the fundamentals of Bayesian methods and MCMC, and the final optional unit explores Gaussian processes. Students will gain practical skills in applying these techniques to real-world problems using R, STAN, and JAGS.

Online Syllabus

Objectives

At the conclusion of this course, students will be able to:

  • Identify the foundational principles of Bayesian statistics.
  • Apply Bayesian methods to parameter estimation and hypothesis testing.
  • Implement MCMC algorithms for complex Bayesian models using STAN and JAGS.
  • Apply Gaussian processes for regression and classification problems using R.
Prerequisites
Faculty
Format

Weekly lectures with demonstration and hands-on exercises. Lectures will be held on Thursdays, 9:00 - 10:30 AM, labs will be held later in the day on Thursdays, 1:30 - 2:30 PM, Jan 8 - Mar 19.

All course materials and handouts will be posted on the course's online syllabus.

Materials

TBA

Grading

TBA

Only UCSF students (defined as individuals enrolled in UCSF degree or certificate programs) will receive academic credit for courses. Official transcripts are available to UCSF students only. A Certificate of Course Completion will be available upon request to individuals who are not UCSF students and satisfactorily pass all course requirements.

UCSF Graduate Division Policy on Disabilities

To Enroll