Introduction to Probability and Inference for Random Signals and Systems

Class Overview

This course is an introduction to modeling and analysis of random phenomena and processes, including the basics of statistical inference in the presence of uncertainty. Topics include probability models, combinatorics, countable and uncountable sample spaces, discrete random variables, probability mass functions, continuous random variables, probability density functions, cumulative distribution functions, expectation and variance, independence and correlation, conditioning and Bayess rule, concentration inequalities, the multivariate Normal distribution, limit theorems (including the law of large numbers and the central limit theorem), Monte Carlo methods, random processes, and the basics of statistical inference. Applications to communications, networking, circuit design, computer engineering, finance, and voting will be discussed throughout the semester.

Prerequisites: MATH 2940 and PHYS 2213, or equivalent.
Key Topics: Probability models, Random Variables, Combinatorics