scholarly journals A transient Markov chain with finitely many cutpoints

Author(s):  
Nicholas James ◽  
Russell Lyons ◽  
Yuval Peres
1992 ◽  
Vol 29 (01) ◽  
pp. 21-36 ◽  
Author(s):  
Masaaki Kijima

Let {Xn, n= 0, 1, 2, ···} be a transient Markov chain which, when restricted to the state space 𝒩+= {1, 2, ···}, is governed by an irreducible, aperiodic and strictly substochastic matrix𝐏= (pij), and letpij(n) =P∈Xn=j, Xk∈ 𝒩+fork= 0, 1, ···,n|X0=i],i, j𝒩+. The prime concern of this paper is conditions for the existence of the limits,qijsay, ofasn →∞. Ifthe distribution (qij) is called the quasi-stationary distribution of {Xn} and has considerable practical importance. It will be shown that, under some conditions, if a non-negative non-trivial vectorx= (xi) satisfyingrxT=xT𝐏andexists, whereris the convergence norm of𝐏, i.e.r=R–1andand T denotes transpose, then it is unique, positive elementwise, andqij(n) necessarily converge toxjasn →∞.Unlike existing results in the literature, our results can be applied even to theR-null andR-transient cases. Finally, an application to a left-continuous random walk whose governing substochastic matrix isR-transient is discussed to demonstrate the usefulness of our results.


2000 ◽  
Vol 14 (1) ◽  
pp. 57-79 ◽  
Author(s):  
Jean-François Dantzer ◽  
Mostafa Haddani ◽  
Philippe Robert

The stability properties of the bandwidth allocation algorithm First Fit are analyzed for some distributions on the sizes of the requests. Fluid limits are used to get the ergodicity results. When there are two possible sizes, the description of the transient behavior involves a finite Markov chain on the exit states of a transient Markov chain on a countable state space. The explicit expression of this exit matrix is given.


1992 ◽  
Vol 29 (1) ◽  
pp. 21-36 ◽  
Author(s):  
Masaaki Kijima

Let {Xn, n = 0, 1, 2, ···} be a transient Markov chain which, when restricted to the state space 𝒩 + = {1, 2, ···}, is governed by an irreducible, aperiodic and strictly substochastic matrix 𝐏 = (pij), and let pij(n) = P ∈ Xn = j, Xk ∈ 𝒩+ for k = 0, 1, ···, n | X0 = i], i, j 𝒩 +. The prime concern of this paper is conditions for the existence of the limits, qij say, of as n →∞. If the distribution (qij) is called the quasi-stationary distribution of {Xn} and has considerable practical importance. It will be shown that, under some conditions, if a non-negative non-trivial vector x = (xi) satisfying rxT = xT𝐏 and exists, where r is the convergence norm of 𝐏, i.e. r = R–1 and and T denotes transpose, then it is unique, positive elementwise, and qij(n) necessarily converge to xj as n →∞. Unlike existing results in the literature, our results can be applied even to the R-null and R-transient cases. Finally, an application to a left-continuous random walk whose governing substochastic matrix is R-transient is discussed to demonstrate the usefulness of our results.


2020 ◽  
Vol 165 ◽  
pp. 108855
Author(s):  
Ivo Adan ◽  
Sergey Foss ◽  
Seva Shneer ◽  
Gideon Weiss

2018 ◽  
Vol 50 (01) ◽  
pp. 1-34
Author(s):  
R. D. Foley ◽  
D. R. McDonald

AbstractWe construct a simple example, surely known to Harry Kesten, of anR-transient Markov chain on a countable state spaceS∪ {δ}, where δ is absorbing. The transition matrixKonSis irreducible and strictly substochastic. We determine the Yaglom limit, that is, the limiting conditional behavior given nonabsorption. Each starting statex∈Sresults in a different Yaglom limit. Each Yaglom limit is anR-1-invariant quasi-stationary distribution, whereRis the convergence parameter ofK. Yaglom limits that depend on the starting state are related to a nontrivialR-1-Martin boundary.


2019 ◽  
Vol 62 (3) ◽  
pp. 577-586 ◽  
Author(s):  
Garnett P. McMillan ◽  
John B. Cannon

Purpose This article presents a basic exploration of Bayesian inference to inform researchers unfamiliar to this type of analysis of the many advantages this readily available approach provides. Method First, we demonstrate the development of Bayes' theorem, the cornerstone of Bayesian statistics, into an iterative process of updating priors. Working with a few assumptions, including normalcy and conjugacy of prior distribution, we express how one would calculate the posterior distribution using the prior distribution and the likelihood of the parameter. Next, we move to an example in auditory research by considering the effect of sound therapy for reducing the perceived loudness of tinnitus. In this case, as well as most real-world settings, we turn to Markov chain simulations because the assumptions allowing for easy calculations no longer hold. Using Markov chain Monte Carlo methods, we can illustrate several analysis solutions given by a straightforward Bayesian approach. Conclusion Bayesian methods are widely applicable and can help scientists overcome analysis problems, including how to include existing information, run interim analysis, achieve consensus through measurement, and, most importantly, interpret results correctly. Supplemental Material https://doi.org/10.23641/asha.7822592


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