Add to ORKGThe paper is devoted on methods and algorithms for steady-state analysis of Markov chains. Basic, direct and iterative methods for steady-state analysis of Markov chains are concerned, where Gaussian Elimination method and Grassman method, as well as Power, Jacobis and Gauss-Seidels methods are implemented. Algorithms for computation of steady-state probability vector for finite Markov chains are developed. Comparison of numerical solutions to exact equilibrium solution for local-balance equation of Discrete-Time Markov Chain is given. Example and numerical results for feedback networks of Markovian queues are shown
International audienceMarkov chains are a fundamental class of stochastic processes. They are widely...
International audienceMarkov chains are a fundamental class of stochastic processes. They are widely...
International audienceMarkov chains are a fundamental class of stochastic processes. They are widely...
Abstract: Two main approximation methods for steady-state analysis of Markov chains are introduced: ...
This paper illustrates the current state of development of an algorithm for the steady state soluti...
AbstractA number of important theorems arising in connection with Gaussian elimination are derived, ...
In this paper, a new method for evaluating the steady-state distribution of an ergodic, discrete or ...
This paper deals with monotone iterative methods for the computation of the steady-state probability...
We present a new numerical method for the computation of the steady-state solution of Markov chains....
AbstractThis paper deals with monotone iterative methods for the computation of the steady-state pro...
This thesis directly exploits the structure contained in the transition diagrams of Markovian queuei...
Theme 1 - Reseaux et systemes. Projet ModelSIGLEAvailable at INIST (FR), Document Supply Service, un...
We treat a special type of Markov chain with a finite state space. This type of Markov chain often a...
International audienceMarkov chains are a fundamental class of stochastic processes. They are widely...
This paper describes and compares several methods for computing stationary probability distributions...
International audienceMarkov chains are a fundamental class of stochastic processes. They are widely...
International audienceMarkov chains are a fundamental class of stochastic processes. They are widely...
International audienceMarkov chains are a fundamental class of stochastic processes. They are widely...
Abstract: Two main approximation methods for steady-state analysis of Markov chains are introduced: ...
This paper illustrates the current state of development of an algorithm for the steady state soluti...
AbstractA number of important theorems arising in connection with Gaussian elimination are derived, ...
In this paper, a new method for evaluating the steady-state distribution of an ergodic, discrete or ...
This paper deals with monotone iterative methods for the computation of the steady-state probability...
We present a new numerical method for the computation of the steady-state solution of Markov chains....
AbstractThis paper deals with monotone iterative methods for the computation of the steady-state pro...
This thesis directly exploits the structure contained in the transition diagrams of Markovian queuei...
Theme 1 - Reseaux et systemes. Projet ModelSIGLEAvailable at INIST (FR), Document Supply Service, un...
We treat a special type of Markov chain with a finite state space. This type of Markov chain often a...
International audienceMarkov chains are a fundamental class of stochastic processes. They are widely...
This paper describes and compares several methods for computing stationary probability distributions...
International audienceMarkov chains are a fundamental class of stochastic processes. They are widely...
International audienceMarkov chains are a fundamental class of stochastic processes. They are widely...
International audienceMarkov chains are a fundamental class of stochastic processes. They are widely...