PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Bortolussi L., Hillston J., Latella D., Massink M. Continuous approximation of collective systems behaviour: a tutorial. Technical report, 2011.
 
 
Abstract
(English)
In this paper we will introduce the reader to the field of deterministic approximation of Markov processes, both in discrete and in continuous time. We will discuss fluid approximation of continuous time Markov chains and mean field approximation of discrete time Markov chains, considering the cases in which the deterministic limit process lives in continuous time or in discrete time. We also discuss some more advanced results, especially those concerned with the limit stationary behaviour. We assume a knowledge of modeling with Markov chains, but not on more advanced topics in stochastic processes.
Subject Deterministic approximation
Fluid approximation
Mean field approximation
Markov Chains
Stochastic process algebras
B.8.2 Performance Analysis and Design Aids
D.2.4 Software/Program Verification
F.1.1 Models of Computation
F.1.2 Modes 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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