|
|
4
|
|
77403
|
Lecture-Discussion
|
A1
|
11:00AM
-12:20PM
|
TR
|
2039 Campus Instructional Facility
|
Wang, Y
|
- Availability:
- CrossListOpen (Restricted)
- Part of Term:
- 1
- Date Range:
- 08/24/26-12/09/26
- Credit:
- 4 hours
- Section Info:
- Topic: Introduction to Sampling with Application to Data Analysis Sampling is a fundamental tool in modern statistics, Bayesian inference, and machine learning. This course covers methods for generating samples from complex probability distributions and developing scalable sampling tools for modern statistical and data analysis problems. The course emphasizes the theory and algorithms behind classical and modern sampling approaches, including Markov chain Monte Carlo, gradient-based methods, and transport-based methods. It also introduces generative modeling frameworks that use sampling ideas, including variational methods, diffusion models, and other probabilistic generative approaches. The course focuses on the computational and theoretical principles underlying these methods rather than programming skills. A solid background in undergraduate linear algebra, analysis, and probability is recommended.
- Restriction(s):
-
Restricted to Graduate - Urbana-Champaign.
Restricted to students in the Statistics department.
|