Two fundamental concepts and quantities, realization factors and performance potentials, are introduced for Markov processes. The relations among these two quantities and the group inverse of the infinitesimal generator are studied. It is shown that the sensitivity of the steady-state performance with respect to the change of the infinitesimal generator can be easily calculated by using either of these three quantities and that these quantities can be estimated by analyzing a single sample path of a Markov process. Based on these results, algorithms for estimating performance sensitivities on a single sample path of a Markov process can be proposed. The potentials in this paper are defined through realization factors and are shown to be the...
In this paper, we present an algorithmic approach for sensitivity analysis of stationary and transie...
International audienceThis paper considers a sensitivity analysis in Hidden Markov Models with conti...
The goal of this paper is two-fold: First, we present a sensitivity point of view on the optimizatio...
We provide algorithms to compute the performance derivatives of Markov chains with respect to change...
Using a sample path approach, we derive a new formula for performance sensitivities of discrete-time...
We study the structure of sample paths of Markov systems by using performance potentials as the fund...
It is known that the performance potentials (or equivalently, perturbation realization factors) can ...
This thesis is dedicated to the applications of performance potential in the sensitivity problems an...
Perturbation realization factor is an important concept in perturbation analysis of both queueing sy...
Sensitivity analysis of Markovian models amounts to computing the constants in polynomial functions...
Abstract: Markov chains are useful to model various complex systems. In numerous situations, the und...
The goals of perturbation analysis (PA), Markov decision processes (MDPs), and reinforcement learnin...
Sensitivity analysis plays an important role in performance optimization of stochastic systems. It p...
We study a fundamental feature of the generalized semi-Markov processes (GSMPs), called event coupli...
Abstract We investigate the sensitivity analysis for a discrete-time queueing system using perturbat...
In this paper, we present an algorithmic approach for sensitivity analysis of stationary and transie...
International audienceThis paper considers a sensitivity analysis in Hidden Markov Models with conti...
The goal of this paper is two-fold: First, we present a sensitivity point of view on the optimizatio...
We provide algorithms to compute the performance derivatives of Markov chains with respect to change...
Using a sample path approach, we derive a new formula for performance sensitivities of discrete-time...
We study the structure of sample paths of Markov systems by using performance potentials as the fund...
It is known that the performance potentials (or equivalently, perturbation realization factors) can ...
This thesis is dedicated to the applications of performance potential in the sensitivity problems an...
Perturbation realization factor is an important concept in perturbation analysis of both queueing sy...
Sensitivity analysis of Markovian models amounts to computing the constants in polynomial functions...
Abstract: Markov chains are useful to model various complex systems. In numerous situations, the und...
The goals of perturbation analysis (PA), Markov decision processes (MDPs), and reinforcement learnin...
Sensitivity analysis plays an important role in performance optimization of stochastic systems. It p...
We study a fundamental feature of the generalized semi-Markov processes (GSMPs), called event coupli...
Abstract We investigate the sensitivity analysis for a discrete-time queueing system using perturbat...
In this paper, we present an algorithmic approach for sensitivity analysis of stationary and transie...
International audienceThis paper considers a sensitivity analysis in Hidden Markov Models with conti...
The goal of this paper is two-fold: First, we present a sensitivity point of view on the optimizatio...