Benefits of Bayesian network models.

The application of Bayesian Networks (BN) or Dynamic Bayesian Networks (DBN) in dependability and risk analysis is a recent development. A large number of scientific publications show the interest in the applications of BN in this field. Unfortunately, this modeling formalism is not fully accepted i...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Weber, Philippe (Co-author of Data uncertainty and important measures) (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Hoboken, NJ : Wiley-ISTE, 2016.
Colección:Systems and industrial engineering series.
Temas:
Bayesian statistical decision theory.
Mathematical models.
Models, Theoretical
Théorie de la décision bayésienne.
Modèles mathématiques.
mathematical models.
MATHEMATICS > General.
MATHEMATICS > Applied.
MATHEMATICS > Probability & Statistics > General.
Bayesian statistical decision theory
Mathematical models
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover ; Title Page ; Copyright ; Contents; Foreword by J.-F. Aubry; Foreword by L. Portinale; Acknowledgments; Introduction; I.1. Problem statement; I.2. Book structure; PART 1. Bayesian Networks; 1. Bayesian Networks: a Modeling Formalism for System Dependability; 1.1. Probabilistic graphical models: BN; 1.1.1. BN: a formalism to model dependability; 1.1.2. Inference mechanism; 1.2. Reliability and joint probability distributions; 1.2.1. Multi-state system example; 1.2.2. Joint distribution; 1.2.3. Reliability computing; 1.2.4. Factorization; 1.3. Discussion and conclusion.
  • 2. Bayesian Network: Modeling Formalism of the Structure Function of Boolean Systems2.1. Introduction; 2.2. BN models in the Boolean case; 2.2.1. BN model from cut-sets; 2.2.2. BN model from tie-sets; 2.2.3. BN model from a top-down approach; 2.2.4. BN model of a bowtie; 2.3. Standard Boolean gates CPT; 2.4. Non-deterministic CPT; 2.5. Industrial applications; 2.6. Conclusion; 3. Bayesian Network: Modeling Formalism of the Structure Function of Multi-State Systems; 3.1. Introduction; 3.2. BN models in the multi-state case; 3.2.1. BN model of multi-state systems from tie-sets.
  • 3.2.2. BN model of multi-state systems from cut-sets3.2.3. BN model of multi-state systems from functional and dysfunctional analysis; 3.3. Non-deterministic CPT; 3.4. Industrial applications; 3.5. Conclusion; PART 2. Dynamic Bayesian Networks; 4. Dynamic Bayesian Networks: Integrating Environmental and Operating Constraints in Reliability Computation; 4.1. Introduction; 4.2. Component modeled by a DBN; 4.2.1. DBN model of a MC; 4.2.2. DBN model of non-homogeneous MC; 4.2.3. Stochastic process with exogenous constraint; 4.3. Model of a dynamic multi-state system.
  • 4.4. Discussion on dependent processes4.5. Conclusion; 5. Dynamic Bayesian Networks: Integrating Reliability Computation in the Control System; 5.1. Introduction; 5.2. Integrating reliability information into the control; 5.3. Control integrating reliability modeled by DBN; 5.3.1. Modeling and controlling an over-actuated system; 5.3.2. Integrating reliability; 5.4. Application to a drinking water network; 5.4.1. DBN modeling; 5.4.2. Results and discussion; 5.5. Conclusion; 5.6. Acknowledgments; Conclusion; Modeling the functional consequences of failures from structured knowledge.
  • Dynamic modeling system reliability based on the reliability of components from the environmentSynthesis of the control law with the aim of optimizing system reliability based on its sensitivity to actuator failures; Bibliography; Index; Other titles from iSTE in Systems and Industrial Engineering
  • Robotics; EULA.

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