Abstract. The solution of continuous and discrete-time Markovian models is still challenging mainly when we model large complex systems, for example, to.

This paper introduces the concepts of backward coupling and the advantages of monotonicity properties and component-wise characteristics to simulate Stochastic ...

This paper introduces the concepts of backward coupling and the advantages of monotonicity properties and component-wise characteristics to simulate Stochastic ...

Perfect Simulation of SAN. Final considerations. Perfect Simulation of. Stochastic Automata Networks. P. Fernandes, J. M. Vincent∗, T. Webber. PUCRS, Porto ...

This paper introduces the concepts of backward coupling and the advantages of monotonicity properties and component-wise characteristics to simulate Stochastic ...

The aim of this tutorial is to introduce the concept of perfect generation and discuss about the algorithmic design of perfect samplers to improve the ...

This paper introduces the concepts of backward coupling and the advantages of monotonicity properties and component-wise characteristics to simulate Stochastic ...

Fingerprint. Dive into the research topics of 'Perfect simulation of stochastic automata networks'. Together they form a unique fingerprint.

Stochastic Automata Networks (SAN) is a powerful formal- ism to describe systems as stochastic models. Through these models we can derive probabilities ...

This paper presents a stochastic modelling framework based on stochastic automata networks (SANs) for the analysis of complex biochemical reaction networks.