Markov Processes for Stochastic Modeling, 1st Edition by Masaaki Kijima (auth.)

By Masaaki Kijima (auth.)

This booklet offers an algebraic improvement of the idea of countable country house Markov chains with discrete- and continuous-time parameters. A Markov chain is a stochastic procedure characterised by way of the Markov prop­ erty that the distribution of destiny relies merely at the present nation, now not regularly background. regardless of its uncomplicated type of dependency, the Markov estate has enabled us to advance a wealthy procedure of suggestions and theorems and to derive many effects which are precious in purposes. in truth, the parts that may be modeled, with various levels of good fortune, via Markov chains are massive and are nonetheless increasing. the purpose of this publication is a dialogue of the time-dependent habit, referred to as the brief habit, of Markov chains. From the sensible perspective, whilst modeling a stochastic procedure by means of a Markov chain, there are various cases within which time-limiting effects akin to desk bound distributions don't have any that means. Or, even if the desk bound distribution is of a few significance, it's always harmful to exploit the desk bound consequence by myself with no understanding the temporary habit of the Markov chain. no longer many books have paid a lot cognizance to this subject, regardless of its seen importance.

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However, 1r Tl is not convergent and so the symmetric random walk is not positive recurrent. It is worth noting that the existence of an invariant vector alone does not imply that {Xn} is recurrent. 4 Finite Markov chains In this section, we assume that the state space is finite and given by N = {0, 1, · · ·, N}. Suppose that a finite Markov chain {Xn} has one recurrent class. 30) where the submatrix Q corresponds to the recurrent class and T the set of transient states. Note that both Q and T are square but R may not be.

2 A transition diagram. Since p;;(O) = 1 > 0, reflexivity holds. Symmetry follows at once from the definition of communication. 7). 3 If i-+ j and j-+ k then i-+ k. We partition the state space into equivalence classes based on communication. The states in an equivalence class are those which communicate with each other. It is possible, starting from one class, to enter some other class with positive probability. However, it is not possible to return to the initial class; for otherwise, the states in the two classes communicate so that they form a single class.

Since Pii ( n) ;::: Pij ( n;j) Pii ( n - n;j), and since M + N ;::: ni + n;j so that n- n;j ;=:: ni, the right-hand side in the above inequality is positive, which is a contradiction. This proves the lemma. 1). 9, if pk has no zero components then neither does pn for all n;::: k. In fact, let 6 min;,j p;j(k) > 0 so that pk 2: bE, where E denotes the matrix whose components are all unity. Then, since P is stochastic, we have PE = E, and so = pk+l = P pk ;::: 6 PE = 6 E > 0. Recall that an irreducible Markov chain is ergodic if it is positive recurrent and aperiodic.

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