Applied Stochastic Analysis
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
Prerequisites & course information
Prerequisites: Elementary probability theory IEOR E3658 or above, stochastic process the first part of IEOR E4106 or STAT G4264, knowledge on analysis MATH GU4601 or above, differential equations APMA E4200 , numerical methods APMA E4300 & programming skills
Sources
- Course catalogue ↗2026-2027