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Prerequisites
multivariate calculus, linear algebra, elementary differential equations (at the level of Math 3D at UCI), and (helpful to know) some elementary probability
Course Description
Tentative topics:
Markov Chains and Linear Difference Equations
Continuous Time Markov Processes
Poisson Counters and Differential Equations
Wiener Processes and Differential Equations
Ito's calculus and Ito Formula
Diffusion, Fokker-Planck Equations
Probability space, Foundation of stochastic processes
Conditional Expectation, Martingales
Markov Properties, Feymann-Kac Theorem, Kolmogorov backward equation
Application to stochastic control
Application to filtering problems
Application to population genetics
Lecture notes
Textbook
Homework
Exam