PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Bortolussi L., Hillston J., Latella D., Massink M. Continuous approximation of collective systems behaviour: a tutorial. In: Performance Evaluation, vol. 70 (5) pp. 317 - 349. Elsevier, 2013. [Online First 01 March 2013]
 
 
Abstract
(English)
In this paper we present an overview of the field of deterministic approximation of Markov processes, both in discrete and continuous times. We will discuss mean field approximation of discrete time Markov chains and fluid approximation of continuous time Markov chains, considering the cases in which the deterministic limit process lives in continuous time or discrete time. We also consider some more advanced results, especially those relating to the limit stationary behaviour. We assume a knowledge of modelling with Markov chains, but not of more advanced topics in stochastic processes.
URL: http://dx.doi.org/10.1016/j.peva.2013.01.001
DOI: 10.1016/j.peva.2013.01.001
Subject Deterministic approximation
Fluid approximation
Mean field approximation
Markov Chains
B.8.2 Performance Analysis and Design Aids
D.2.4 Software/Program Verification
F.1.2 Modes of Computation
F.1.1 Models of Computation
F.3.1 Specifying and Verifying and Reasoning about Programs
G.1.7 Ordinary Differential Equations
G.3 PROBABILITY AND STATISTICS
34-XX ORDINARY DIFFERENTIAL EQUATIONS
60Jxx Markov processes


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