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, de sécurité ? s'écrit P (? | M ). Afin de rester cohérent avec les notations du chapitre précédent, nous écrivons P (D | M ) pour la vraisemblance des données connaissant le modèle (structure et paramètres). Le problème que nous posons peut être formalisé comme suit: ?M * , M * = argmax(P (D | M )) avec P (? | M )

, Les propriétés de sécurité que nous cherchons à vérifier sont qualitatives et traitent généralement un nombre limité d'événements dans notre modèle

. Intuitivement, nous avons tendance à traiter ce type de problèmes comme des problèmes d'optimisation. Cependant, le problème énoncé dans l'équation 4.5 ne peut pas être résolu en utilisant une heuristique d'optimisation multi-objectif telle que [Mir+16], car une propriété qualitative ne peut pas être optimisée, elle est soit vraie soit fausse. En d'autres termes, nous n'avons que des modèles qui ne sont pas "sûrs

, des modèles qui sont "sûrs" (P (? | M ) > c). Par exemple, considérons deux modèles M et M qui satisfont P (? | M ) > c et P (? | M ) > c afin que les deux soient "sécurisés"; avoir P (? | M ) > P (?midM ) ne signifie pas que M est

M. , Par conséquent, l'équation 4.5 ne peut pas être optimisée à l'aide d'une fonction multi-objectif, alors nous la décomposons et procédons autrement

, La stratégie proposée peut être représentée à l'aide d'un algorithme générique composé de trois étapes principales, la première étape étant la phase d'apprentissage, la deuxième étape étant la phase d'exploration de l'espace modèle et la vérification du modèle