Introduction to Markov Chains: With Special Emphasis on Rapid Mixing
Jul 2014 · Vieweg+Teubner Verlag
Ebook
234
Pages
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About this ebook
The aims of this book are threefold:
We start with a naive description of a Markov chain as a memoryless random walk on a finite set. This is complemented by a rigorous definition in the framework of probability theory, and then we develop the most important results from the theory of homogeneous Markov chains on finite state spaces.
Chains are called rapidly mixing if all of the associated walks, regardles of where they started, behave similarly already after comparitively few steps: it is impossible from observing the chain to get information on the starting position or the number of steps done so far. We will thoroughly study methods which have been proposed in the last decades to investigate this phenomenon.
A number of examples will be studied to indicate how the methods treated in this book can be applied.
We start with a naive description of a Markov chain as a memoryless random walk on a finite set. This is complemented by a rigorous definition in the framework of probability theory, and then we develop the most important results from the theory of homogeneous Markov chains on finite state spaces.
Chains are called rapidly mixing if all of the associated walks, regardles of where they started, behave similarly already after comparitively few steps: it is impossible from observing the chain to get information on the starting position or the number of steps done so far. We will thoroughly study methods which have been proposed in the last decades to investigate this phenomenon.
A number of examples will be studied to indicate how the methods treated in this book can be applied.
About the author
Prof. Dr. Ehrhard Behrends ist am Fachbereich Mathematik der Freien Universität Berlin tätig.
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