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Markov Chains by J. R. Norris

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Introduction

This book is about a certain sort of random process. The characteristic property of this sort of process is that it retains no memory of where it has been in the past. This means that only the current state of the process can influence where it goes next. Such a process is called a Markov process. We shall be concerned exclusively with the case where the process can assume only a finite or countable set of states, when it is usual to refer it as a Markov chain.

Examples of Markov chains abound, as you will see throughout the book. What makes them important is that not only do Markov chains model many phenomena of interest, but also the lack of memory property makes it possible to predict how a Markov chain may behave, and to compute ...

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