The problem of computing bounds on the conditional steady-state probability vector of a subset of states in finite, ergodic discrete-time Markov chains (DTMCs) is considered. An improved algorithm utilizing the strong stochastic (st-)order is given. On standard benchmarks from the literature and other examples, it is shown that the proposed algorithm performs better than the existing one in the strong stochastic sense. Furthermore, in certain cases the conditional steady-state probability vector of the subset under consideration can be obtained exactly. Copyright 2006 ACM
Imprecise continuous-time Markov chains are a robust type of continuous-time Markov chains that allo...
In this paper, it is shown that nearly completely decomposable (NCD) Markov chains are quasi-lumpabl...
Cataloged from PDF version of article.We propose a bounding technique for the equilibrium probabilit...
Cataloged from PDF version of article.This paper presents an improved version of a componentwise bou...
This paper presents an improved version of a componentwise bounding algorithm for the state probabil...
Two new algorithms are proposed for the computation of bounds for the steady-state reward rate of ir...
AbstractIn this report we relate the property of stochastic boundedness to the existence of stationa...
International audienceWe prove new iterative algorithms to provide component-wise bounds of the stea...
International audienceConsider a stochastic process X on a finite state space X = {1,. .. , d}. It i...
In this paper, a new method for evaluating the steady-state distribution of an ergodic, discrete or ...
Abstract: Two main approximation methods for steady-state analysis of Markov chains are introduced: ...
AbstractThis paper deals with monotone iterative methods for the computation of the steady-state pro...
A method to bound the steady-state solution of large Markov chains is presented. It integrates the c...
International audienceWe combine monotone bounds of Markov chains and the coupling from the past to ...
AbstractMarkov chains have always constituted an efficient tool to model discrete systems. Many perf...
Imprecise continuous-time Markov chains are a robust type of continuous-time Markov chains that allo...
In this paper, it is shown that nearly completely decomposable (NCD) Markov chains are quasi-lumpabl...
Cataloged from PDF version of article.We propose a bounding technique for the equilibrium probabilit...
Cataloged from PDF version of article.This paper presents an improved version of a componentwise bou...
This paper presents an improved version of a componentwise bounding algorithm for the state probabil...
Two new algorithms are proposed for the computation of bounds for the steady-state reward rate of ir...
AbstractIn this report we relate the property of stochastic boundedness to the existence of stationa...
International audienceWe prove new iterative algorithms to provide component-wise bounds of the stea...
International audienceConsider a stochastic process X on a finite state space X = {1,. .. , d}. It i...
In this paper, a new method for evaluating the steady-state distribution of an ergodic, discrete or ...
Abstract: Two main approximation methods for steady-state analysis of Markov chains are introduced: ...
AbstractThis paper deals with monotone iterative methods for the computation of the steady-state pro...
A method to bound the steady-state solution of large Markov chains is presented. It integrates the c...
International audienceWe combine monotone bounds of Markov chains and the coupling from the past to ...
AbstractMarkov chains have always constituted an efficient tool to model discrete systems. Many perf...
Imprecise continuous-time Markov chains are a robust type of continuous-time Markov chains that allo...
In this paper, it is shown that nearly completely decomposable (NCD) Markov chains are quasi-lumpabl...
Cataloged from PDF version of article.We propose a bounding technique for the equilibrium probabilit...