EECS 422: Random Processes in Communications and Control I
Winter 2014


Lecture 1Introduction, Probability spaces, properties of probability measures, conditional probability, statistical independence.
Lecture 2 Conditional independence, repeated trials, random variables, CDFs, PMFs, PDFs, mixed and singular random variables.
Lecture 3 Multiple random variables, independent RVs, conditioning and RVs, stochastic processes, the Bernoulli process.
Lecture 4 Expectations, functions of random variables, moment generating functions, conditional expectation.
Lecture 5Markov Inequality, Chebyshev's inequality, Chernoff Bounds.
Lecture 6 Convergence of random variables, laws of large numbers, central limit theorem.
Lecture 7 Central Limit theorem cont'd., Poisson Processes.
Lecture 8 Poisson Processes cont'd.
Lecture 9 Markov Chains, transistion matrices/graphs, first step analysis.

A list of lecture topics from 2013 can be found here.