AbstractThis paper deals with monotone iterative methods for the computation of the steady-state probability vector of irreducible Markov chains. The emphasis is laid on verification techniques aiming at deriving, from an approximation, true bounds on the solution
This paper describes and compares several methods for computing stationary probability distributions...
This paper is concerned with an iteration for determining the steady-state probability vector of a n...
We consider the value 1 problem for probabilistic automata over finite words: it asks whether a give...
AbstractThis paper deals with monotone iterative methods for the computation of the steady-state pro...
This paper deals with monotone iterative methods for the computation of the steady-state probability...
The paper is a review of results on the asymptotic behavior of Markov processes generated by i.i.d. ...
AbstractMarkov chains have always constituted an efficient tool to model discrete systems. Many perf...
AbstractA number of important theorems arising in connection with Gaussian elimination are derived, ...
International audienceWe combine monotone bounds of Markov chains and the coupling from the past to ...
AbstractA stochastic matrix is “monotone” [4] if its row-vectors are stochastically increasing. Clos...
International audienceSimulation approaches are alternative methods to estimate the stationary be- h...
Abstract: Two main approximation methods for steady-state analysis of Markov chains are introduced: ...
In this paper we deal with iterative numerical methods to solve linear systems arising in continuous...
This paper deals with the computation of invariant measures and stationary expectations for discrete...
We discuss the recently introduced multilevel algorithm for the steady-state solution of Markov chai...
This paper describes and compares several methods for computing stationary probability distributions...
This paper is concerned with an iteration for determining the steady-state probability vector of a n...
We consider the value 1 problem for probabilistic automata over finite words: it asks whether a give...
AbstractThis paper deals with monotone iterative methods for the computation of the steady-state pro...
This paper deals with monotone iterative methods for the computation of the steady-state probability...
The paper is a review of results on the asymptotic behavior of Markov processes generated by i.i.d. ...
AbstractMarkov chains have always constituted an efficient tool to model discrete systems. Many perf...
AbstractA number of important theorems arising in connection with Gaussian elimination are derived, ...
International audienceWe combine monotone bounds of Markov chains and the coupling from the past to ...
AbstractA stochastic matrix is “monotone” [4] if its row-vectors are stochastically increasing. Clos...
International audienceSimulation approaches are alternative methods to estimate the stationary be- h...
Abstract: Two main approximation methods for steady-state analysis of Markov chains are introduced: ...
In this paper we deal with iterative numerical methods to solve linear systems arising in continuous...
This paper deals with the computation of invariant measures and stationary expectations for discrete...
We discuss the recently introduced multilevel algorithm for the steady-state solution of Markov chai...
This paper describes and compares several methods for computing stationary probability distributions...
This paper is concerned with an iteration for determining the steady-state probability vector of a n...
We consider the value 1 problem for probabilistic automata over finite words: it asks whether a give...
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