Applied Stochastic Analysis

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.