Titlebook: Classical and Spatial Stochastic Processes; With Applications to Rinaldo B. Schinazi Textbook 20142nd edition Springer Science+Business Med

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Discrete Time Markov Chains,Branching processes and random walks are examples of Markov chains. In this chapter we study general properties of Markov chains.

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Stationary Distributions for Discrete Time Markov Chains,We continue the study of Markov chains initiated in Chap.?5. A stationary distribution is a stochastic equilibrium for the chain. We find conditions under which such a distribution exists. We are also interested in conditions for convergence to a stationary distribution.

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The Poisson Process,In this chapter we introduce a continuous time stochastic process called the Poisson process. It is a good model in a number of situations and it has many interesting mathematical properties. There is a strong link between the exponential distribution and the Poisson process. This is why we start by reviewing the exponential distribution.

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