This paper examines numerical issues in computing solutions to networks of stochastic
automata. It is well-known that when the matrices that represent the automata contain only …

A Stochastic Automata Network (SAN) consists of a number of individual stochastic
automata that operate more or less independently of each other. Each individual automaton …

Two types of techniques are usually adopted in language differentiation: token matching and
statistical analysis. In this paper we present a method which uses a combined analysis of …

Many very large Markov chains can be modelled efficiently as stochastic automata networks
(SANs). A SAN is composed of individual automata which, for the most part, act …

We present a new methodology based on the stochastic ordering, algorithmic derivation of
simpler Markov chains and numerical analysis of these chains. The performance indices …

Markov chains have become an accepted technique for modeling a great variety of
situations. They have been in use since the early 1900's, but it is only in recent years with …

Stochastic automata networks (SANs) have recently received much attention in the literature
as a means to analyze complex Markov chains in an efficient way. The main advantage of …

We present a new kind of synchronization which allows Continuous Time Stochastic
Automata Networks (SAN) to have a product form steady-state distribution. Unlike previous …

Stochastic automata networks (SANs) have been developed and used in the last fifteen
years as a modeling formalism for large systems that can be decomposed into loosely …

The generator matrix of a continuous-time stochastic automata network (SAN) is a sum of
tensor products of smaller matrices, which may have entries that are functions of the global …