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     <dc:title xml:lang="en">Detection and quantiﬁcation of events in stochastic systems </dc:title>
     <dcterms:alternative xml:lang="fr">Détection et quantiﬁcation d’événements dans les systèmes stochastiques </dcterms:alternative>
     <dc:subject xml:lang="fr">Systèmes stochastiques</dc:subject><dc:subject xml:lang="fr">Information partielle</dc:subject><dc:subject xml:lang="fr">Diagnosticabilité</dc:subject><dc:subject xml:lang="fr">Classiﬁcation</dc:subject><dc:subject xml:lang="fr">Apprentissage</dc:subject><dc:subject xml:lang="fr">Vériﬁcation de modèles
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     <dc:subject xml:lang="en">Stochastic systems</dc:subject><dc:subject xml:lang="en">Partial information</dc:subject><dc:subject xml:lang="en">Diagnosability</dc:subject><dc:subject xml:lang="en">Classiﬁcation</dc:subject><dc:subject xml:lang="en">Learning</dc:subject><dc:subject xml:lang="en">Model-checking
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						<tef:elementdEntree autoriteSource="Sudoc" autoriteExterne="02781002X">Systèmes stochastiques</tef:elementdEntree>
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						<tef:elementdEntree autoriteSource="Sudoc" autoriteExterne="203675789">Vérification de modèles (informatique)</tef:elementdEntree>
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     <dcterms:abstract xml:lang="fr">Les systèmes stochastiques à information partielle permettent de représenter de nombreux systèmes dont les paramètres sont inconnus et dont le fonctionnement dépend de facteurs en dehors de notre contrôle. Dans cette thèse, nous étudions plusieurs problèmes liés à ces systèmes. Le premier est la diagnosticabilité, c’est-à-dire la capacité de décider si un évènement particulier s’est produit. Le second est la classiﬁcation qui est la capacité de décider à partir d’une trace d’une exécution quel système l’a produite. Enﬁn,  nous nous intéressons aux garanties que l'on peut avoir quand on apprend les probabilités de transition d’un système stochastique.
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     <dcterms:abstract xml:lang="en"> Stochastic systems with partial information allow one to represent numerous systems whose parameters are unknown and whose operation may depend on out-of-control factors. In this thesis, we study several problems linked to these systems. First one is diagnosability, that is the capacity to decide if a particular event occurred. Second one is classiﬁcation which is the capacity to decide from a trace of an execution which system produced it. Finally, we are interested in the guarantees one can obtain by learning the probability transitions of a stochastic system.</dcterms:abstract>
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