Course Description
The basic limit theorems of probability theory and their application to maximum likelihood estimation. Basic Monte Carlo methods and importance sampling. Markov chains and processes, random walks, basic ergodic theory and its application to parameter estimation. Discrete time stochastic control and Bayesian filtering. Diffusion approximations, Brownian motion and an introduction to stochastic differential equations. Examples and problems from various applied areas.
CME 308
Units: 3
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Typically Offered
Not Available
Grading Basis
Letter or Credit/No Credit
Class Level
Graduate
Instructional Mode
In Person
Gen Ed Requirement(s)
None