STOCHASTIC COMPUTATIONAL METHODS

MATH 717

Course Description

Introduction to computational methods that use stochastic algorithms and/or methods that are applied to random or stochastic mathematical problems. The main emphasis will be placed on learning practical tools, while some aspects of theoretical foundations will also be covered (e.g., basic error analysis for numerical solution of stochastic differential equations (SDEs), and basic convergence of Monte Carlo methods). Topics include Monte Carlo methods, Bayesian inference and Bayesian sampling, simulation of Markov chains, numerical analysis for SDEs, data assimilation / state estimation, stochastic optimization methods and random sketching. Applications to science, engineering, finance, data science, and other practical problems also included.

Prerequisties

Graduate/professional standing or declared in Mathematics Visiting International Student Program (graduate or dissertator)

Satisfies

This course does not satisfy any prerequisites.

Credits

Offered

Not Applicable

Grade Point Average

3.94

-0.3% from Historical

Completion Rate

100%

No change from Historical

A Rate

92.5%

-0.77% from Historical

Class Size

35.59% from Historical

Instructors (2025 Fall)

Sorted by ratings from Rate My Professors

NAN CHEN

[email protected]

4.6

Rating

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