Markov chain

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The concept of a Markov chain was developed by Andrey Andreyevich Markov. A Markov chain is a sequence of random variables with the property that it is forgetful of all but its immediate past. For a process {\mathbf \Phi} evolving on a space {\mathsf X} and governed by an overall probability law {\mathsf P} to be a time-homogeneous Markov chain there must be a set of "transition probabilities" \{P^n (x,A), x \in  {\mathsf X}, A \subset {\mathsf X}\} for appropriate sets A such that for times n,m in {\mathbb Z}_+ (Ref. 1 Eq. 1.1)

{\mathsf P} (\Phi_{n+m} \in A \vert \Phi_j,j \leq m; \Phi_m =x)= P^n(x,A);

that is P^n(x,A) denotes the probability that a chain at x will be in the set A after n steps, or transitions. The independence of P^n on the values of \Phi_j,j \leq m is the Markov property, and the independence of P^n and m is the time-homogeneity property.

References[edit]

  1. S. P. Meyn and R. L. Tweedie "Markov Chains and Stochastic Stability", Springer-Verlag, London (1993)
  2. Ruichao Ren and G. Orkoulas "Parallel Markov chain Monte Carlo simulations", Journal of Chemical Physics 126 211102 (2007)