CEM

Introduction to Probability and Random Processes with Applications
Instructors Richard Murray (CDS/BE) Lectures: Tu/Th, 9-10:30, 105 ANB Office hours: by request	Teaching Assistants John Bruer (ACM), Yutong Chen (ACM), May Eaton (EE), Alex Gittens (ACM) Office hours: Fri, 3-4 pm, 213 ANB; Mon, 7-9 pm, 107 ANB.

Course 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.