SANGE -Stochastic Automata Networks Generator. A tool to efficiently predict events through structured Markovian models (extended version)

Abstract : The use of stochastic formalisms, such as Stochastic Automata Networks (SAN), can be very useful for statistical prediction and behavior analysis. Once well fitted, such formalisms can generate probabilities about a target reality.These probabilities can be seen as a statistical approach of knowledge discovery.However, the building process of models for real world problems is time consuming even for experienced modelers. Furthermore, it is often necessary to be a domain specialist to create a model.This work illustrates a new method to automatically learn simple SAN models directly from a data source.This method is encapsulated in a tool called SAN GEnerator (SANGE). Through examples we show how this new model fitting method is powerful and relatively easy to use; therefore this can grant access to a much broader community to such powerful modeling formalisms.
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Rapport
[Research Report] RR-8724, Inria Rhône-Alpes; Grenoble University; Pontifícia Universidade Católica do Rio Grande do Sul; INRIA. 2015
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https://hal.inria.fr/hal-01149604
Contributeur : Joaquim Assunção <>
Soumis le : jeudi 21 mai 2015 - 09:58:52
Dernière modification le : mardi 13 décembre 2016 - 15:42:39
Document(s) archivé(s) le : lundi 14 septembre 2015 - 20:16:47

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  • HAL Id : hal-01149604, version 1

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Joaquim Assunção, Paulo Fernandes, Lucelene Lopes, Angelika Studeny, Jean-Marc Vincent. SANGE -Stochastic Automata Networks Generator. A tool to efficiently predict events through structured Markovian models (extended version). [Research Report] RR-8724, Inria Rhône-Alpes; Grenoble University; Pontifícia Universidade Católica do Rio Grande do Sul; INRIA. 2015. <hal-01149604>

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