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Markov chains with stationary transition probabilities

Markov chains with stationary transition probabilities

Name: Markov chains with stationary transition probabilities

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Language: English

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Access this title on SpringerLink – Click here! Mathematics Probability Theory and Stochastic Processes · Grundlehren der mathematischen Wissenschaften. The theory of Markov chains, although a special case of Markov processes, is here developed for its own sake and presented on its own merits. In general, the . Probability, Random Variables, and Stochastic Processes—Second Edition Markov Chains with Stationary Transition Probabilities (Kai Lai Chung).

Markov chains on a countable state space are studied under the assumption that the transition probabilities (P n (x, y)) constitute a stationary stochastic process. An introductory section exposing some basic results of Nawrotzki and Cogburn is followed by four sections of new results. A stochastic process {Xn; n = 0, 1, } in discrete time with finite or infinite state space S is a Markov Chain with stationary transition probabilities if it satisfies: (1) For each n ≥ 1, if A is an event depending only on any subset of {Xn−1,Xn−2,, 0}, then, for any states i and j in S, P(Xn+1 = j. consider a stationary stochastic sequence (Pn), where each Pn is a stochastic Markov chains with random transition probabilities has been pursued for.

Harris was led to consider the probabilities of hitting one state before another, starting from a Markov chains 2 with stationary transition proba- bilities (DMCS) . A Markov chain is "a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event". In probability theory and related fields, a Markov process, named after the . For example, the transition probabilities from 5 to 4 and 5 to 6 are both transition probability in a finite Markov chain. It is shown that the where E stands for the expectation under the stationary distribution. Classical Edgeworth. Stationary distribution vectors p∞ for Markov chains with associated insight into the sensitivity of p∞ to perturbations in the transition probabilities of T and to . An important assumption in this modelling of owner payment behaviour is that the transition probability matrices are stationary. We study this assumption: the.


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