In this lesson on Markov matrices, steady state, Fourier series, and projections, the lecturer explains the properties of Markov matrices and how they are connected to probability ideas. The focus is on eigenvalues and eigenvectors, and the lecture explores the steady state of a system, which corresponds to an eigenvalue of 1 and its eigenvector. The eigenvector has all positive components, and the lecture goes on to discuss the applications of Markov matrices in population movements, such as the populations of California and Massachusetts.
Markov Matrices, Steady State, Fourier Series, and Projections -- Lecture 24a. A lesson all about applications of Eigenvalues and Eigenvectors.
Gilbert Strang, 18.06 Linear Algebra, Spring 2005. (Massachusetts Institute of Technology: MIT OpenCourseWare), http://ocw.mit.edu (Accessed November 23, 2008). License: Creative Commons BY-NC-SA.
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