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MARKOV CHAINS AND RANDOM WALKS

AI-generated Abstract

This paper provides an introduction to Markov chains, focusing on discrete-time processes where the present state influences future states without dependence on past states. It explores various examples that illustrate these principles, such as those from mathematics and physics, and discusses implications related to probability and analysis within this framework.