Research Article
Stochasticity of probabilistic systems: analysis methodologies case-study
@INPROCEEDINGS{10.1109/COLCOM.2005.1651267, author={Anwitaman Datta and Martin Hasler and Karl Aberer}, title={Stochasticity of probabilistic systems: analysis methodologies case-study}, proceedings={Workshop on Stochasticity in Distributed Systems}, publisher={IEEE}, proceedings_a={STODIS}, year={2006}, month={7}, keywords={Algorithm design and analysis Distributed computing Distribution functions Equations Information analysis Large-scale systems Probability distribution Steady-state Stochastic processes Stochastic systems}, doi={10.1109/COLCOM.2005.1651267} }- Anwitaman Datta
Martin Hasler
Karl Aberer
Year: 2006
Stochasticity of probabilistic systems: analysis methodologies case-study
STODIS
ICST
DOI: 10.1109/COLCOM.2005.1651267
Abstract
We do a case study of two different analysis techniques for studying the stochastic behavior of a randomized system/algorithms: (i) The first approach can be broadly termed as a mean value analysis (MVA), where the evolution of the mean state is studied assuming that the system always actually resides in the mean state; (ii) The second approach looks at the probability distribution function of the system states at any time instance, thus studying the evolution of the (probability mass) distribution function (EoDF).
Copyright © 2005–2024 ICST
