ACM 116 - Introduction to Stochastic Processes and Modeling

Institution:

California Institute of Technology

Subject:

Applied and Computational Mathematics

Description:

Introduction to fundamental ideas and techniques of stochastic analysis and modeling. Random variables, expectation and conditional expectation, joint distributions, covariance, moment generating function, central limit theorem, weak and strong laws of large numbers, discrete time stochastic processes, stationarity, power spectral densities and the Wiener-Khinchine theorem, Gaussian processes, Poisson processes, Brownian motion. The course develops applications in selected areas such as signal processing (Wiener filter), information theory, genetics, queuing and waiting line theory, and finance. Instructor: Tropp.

Credits:

9.00

Credit Hours:

Prerequisites:

Corequisites:

Exclusions:

Level:

Instructional Type:

Lecture

Notes:

Additional Information:

Historical Version(s):

Institution Website:

www.caltech.edu

Phone Number:

(626) 395-6811

Regional Accreditation:

Western Association of Schools and Colleges

Calendar System:

Quarter

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