This course aims to introduce the basic methodology, theory, and algorithms for probabilistic modeling. Specifically, the course is divided into three parts. The first part (and majority) of the course will cover fundamentals of statistical inference, including likelihood-based estimation methods, asymptotic theory, Bayesian statistics, nonparametric models (e.g. nonparametric regression, Gaussian process), minmax theory. The second part will focus on statistical sampling algorithms, with emphasis on Markov Chain Monte Carlo methods. The last part (if time permits) will cover inference for sequential data, with emphasis on filtering and data assimilation.
Gopher Grades is maintained by Social Coding with data from Summer 2017 to Spring 2025 provided by the University in response to a public records request