6.262 Discrete Stochastic Processes
6.262 Discrete Stochastic Processes (Spring 2011, MIT OCW). Instructor: Professor Robert Gallager. Discrete stochastic processes are essentially probabilistic systems that evolve in time via random changes occurring at discrete fixed or random intervals. This course aims to help students acquire both the mathematical principles and the intuition necessary to create, analyze, and understand insightful models for a broad range of these processes. The range of areas for which discrete stochastic-process models are useful is constantly expanding, and includes many applications in engineering, physics, biology, operations research and finance. (from ocw.mit.edu)
|Lecture 08 - Markov Eigenvalues and Eigenvectors|
This lecture covers eigenvalues and eigenvectors of the transition matrix and the steady-state vector of Markov chains. It also includes an analysis of a 2-state Markov chain and a discussion of the Jordan form.
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