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Benefits of Bayesian Network Models by Christophe Simon, Philippe Weber

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Foreword by J.-F. Aubry

Systems Dependability Assessment is the title of a series of books, of which this is the third. The preface to the first series described the reasons why the authors embarked upon writing these books: in recent decades, they have made significant contributions to recent approaches to the predictive dependability of systems by considering concepts developed in other scientific fields but not yet applied to account of dependability. All these authors belong to the Automatic Control Research Center (CRAN, Centre de Recherches en Automatique de Nancy) of the University of Lorraine, France, a research laboratory whose activities are widely oriented towards the diagnosis, reliability, maintainability and safety of systems, which can be described in one word: dependability.

Assessment must be understood as the set of means, methods and tools to provide quantitative measures of dependability, and in these books we are interested in providing predictive measures by using probabilistic approaches. The first two books were dedicated to methods based on the frequentist knowledge of basic elements of a system and on models describing how the failure of this system depends on those of its components. These models are essentially of state-transition type, such as finite state automata and petri nets, and results were obtained by analytic or simulation approaches according to the level of complexity introduced, and data inputs for these models were probabilistic distributions ...

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