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Spring 2022 Applied Mathematics E4306 section 001
Applied Stochastic Analysis
Applied Stochastic Analys

Call Number 12943
Day & Time
Location
MW 10:10am-11:25am
627 Seeley W. Mudd Building
Points 3
Grading Mode Standard
Approvals Required None
Instructor Kui Ren
Type LECTURE
Method of Instruction In-Person
Course Description Non Course Prerequisites: Elementary probability theory (IEOR E3658 or above) and stochastic process (on the level of the first part of IEOR E4106 or STAT G4264) are required. Knowledge on analysis (MATH GU4601 or above) and differential equations (APMA E4200 or above) are required. Knowledge on numerical methods (APMA E4300 and above) and programming skills are required. Provides elementary introduction to fundamental ideas in stochastic analysis for applied mathematics. Core material includes: (i) review of probability theory (including limit theorems), and introduction to discrete Markov chains and Monte Carlo methods; (ii) elementary theory of stochastic process, Ito's stochastic calculus and stochastic differential equations; (iii) introductions to probabilistic representation of elliptic partial differential equations (the Fokker-Planck equation theory); (iv) stochastic approximation algorithms; and (v) asymptotic analysis of SDEs.
Web Site Vergil
Department Applied Physics and Applied Mathematics
Enrollment 39 students (50 max) as of 3:13PM Friday, May 20, 2022
Subject Applied Mathematics
Number E4306
Section 001
Division School of Engineering and Applied Science: Graduate
Open To Barnard College, Columbia College, Engineering:Undergraduate, Engineering:Graduate, GSAS, General Studies
Campus Morningside
Section key 20221APMA4306E001

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SIS update 05/20/22 15:13    web update 05/20/22 15:33