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NOTE: Course information changes frequently, including Methods of Instruction. Please revisit these pages periodically for the most recent and up-to-date course information. | |
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 9:04AM Monday, November 28, 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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