A large variety of product-form solutions for continuous-time Markovian models can be derived by checking a set of structural properties of the underlying stochastic processes and a condition on their reversed rates. In previous work (Marin and Vigliotti (2010)) we have shown how to derive a large class of product-form solutions using a different formulation of the Reversed Compound Agent Theorem (GRCAT). We continue this line of work by showing that it is possible to exploit this result to approximate the steady-state distribution of non-product-form model interactions by means of product-form ones.

Algorithmic product-form approximations of interacting stochastic models

MARIN, Andrea;
2012-01-01

Abstract

A large variety of product-form solutions for continuous-time Markovian models can be derived by checking a set of structural properties of the underlying stochastic processes and a condition on their reversed rates. In previous work (Marin and Vigliotti (2010)) we have shown how to derive a large class of product-form solutions using a different formulation of the Reversed Compound Agent Theorem (GRCAT). We continue this line of work by showing that it is possible to exploit this result to approximate the steady-state distribution of non-product-form model interactions by means of product-form ones.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/37069
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