Course outline: The course will follow the pedagogical format of CW
Gardiner - Handbook of Stochastic Methods. Random variables. Important
discrete and continuous distributions. Stochastic processes. The Markov
description. Chapman-Kolmogorov equation. Analysis of Markov processes.
Brownian motion. Applications to chemical kinetics and biophysical
systems. Master equation. Langevin and Fokker-Planck representations.
Simulating stochastic processes. Gillespie algorithm. First passage time
problems.
Course outcome: Students should develop familiarity
with stochastic descriptions. They should be able to write down the
stochastic description of various systems of interest. They will be able
to use basic tools from linear algebra to derive steady-state
properties and some time-dependent features of such stochastic
processes. They will be able to develop simulations of stochastic
processes under various approximations. The focus is on broad
understanding and applicability rather than deep technical results.
Instructor: Mukund Thattai
Schedule: Monday, Wednesday - 2:00 to 3:30pm
Venue: LH-2
- Teacher: Mukund Thattai