A Calculus of Stochastic Systems for the specification, simulation, and hidden state estimation of hybrid stochastic/non-stochastic systems View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

1995

AUTHORS

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

ABSTRACT

In this paper, we consider mixed systems containing both stochastic and non-stochastic3 components. To compose such systems, we introduce a general combinator which allows the specification of an arbitrary mixed system in terms of elementary components 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 mixed 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 dependencyrelations 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 mixed 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 will appear in Theoretical Computer Science, and is available [1]. More... »

PAGES

21-44

Book

TITLE

Hybrid Systems II

ISBN

978-3-540-60472-3
978-3-540-47519-4

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/3-540-60472-3_2

DOI

http://dx.doi.org/10.1007/3-540-60472-3_2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1031353981


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