A calculus of stochastic systems View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

1994

AUTHORS

Albert Benveniste , Bernard C. Levy , Eric Fabre , Paul Le Guernic

ABSTRACT

In this paper, we consider hybrid systems containing both stochastic and non-stochastic components. To compose such systems, we introduce a general combinator which allows the specification of an arbitrary hybrid system in terms of elementary primitives of only two types. Thus, systems are obtained hierarchically, by composing subsystems, where each subsystem can be viewed as an “increment” in the decomposition of the full system. The resulting hybrid stochastic system specifications are generally not “executable”, since they do not necessarily permit the incremental simulation of the system variables. Such a simulation requires compiling the dependency relations existing between the system variables. Another issue involves finding the most likely internal states of a stochastic system from a set of observations. We provide a small set of primitives for transforming hybrid systems, which allows the solution of the two problems of incremental simulation and estimation of stochastic systems within a common framework. The complete model is called CSS (a Calculus of Stochastic Systems), and is implemented by the Sig language, derived from the Signal synchronous language. Our results are applicable to pattern recognition problems formulated in terms of Markov random fields or hidden Markov models (HMMs), and to the automatic generation of diagnostic systems for industrial plants starting from their risk analysis. A full version of this paper is available [1], omitted proofs can be found in this reference. More... »

PAGES

149-169

Book

TITLE

Formal Techniques in Real-Time and Fault-Tolerant Systems

ISBN

978-3-540-58468-1
978-3-540-48984-9

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/3-540-58468-4_164

DOI

http://dx.doi.org/10.1007/3-540-58468-4_164

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1051569008


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