53. Understanding Linear Algebra 4.5: Markov Chains, Eigenvalues, and Steady-State Vectors
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53. Understanding Linear Algebra 4.5: Markov Chains, Eigenvalues, and Steady-State Vectors
In this video, we see one of the most important applications of eigenvalues and eigenvectors: Markov chains. Using a model of exercise habits in a population, we build a transition matrix, find its eigenvalues and eigenvectors, and use powers of the matrix to predict the long-term behavior of the system. Along the way, we introduce steady-state ...
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