LEADER 04945nam 2200625 450 001 9910818395603321 005 20200520144314.0 010 $a1-119-34731-9 010 $a1-119-34744-0 010 $a1-119-34745-9 035 $a(CKB)3710000000840954 035 $a(EBL)4658585 035 $a(Au-PeEL)EBL4658585 035 $a(CaPaEBR)ebr11251742 035 $a(CaONFJC)MIL950696 035 $a(PPN)203990250 035 $a(OCoLC)957655787 035 $a(CaSebORM)9781848219922 035 $a(MiAaPQ)EBC4658585 035 $a(EXLCZ)993710000000840954 100 $a20160916d2016 uy| 0 101 0 $aeng 135 $aur|n|---||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aBenefits of Bayesian network models /$fPhilippe Weber, Christophe Simon 210 1$aHoboken, NJ :$cWiley,$d2016. 215 $a1 online resource (151 p.) 225 0 $aSystems dependability assessment set ;$vvolume 2 300 $aDescription based upon print version of record. 311 $a1-84821-992-X 320 $aIncludes bibliographical references and index. 327 $aCover ; 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 327 $a2. 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 327 $a3.2.2. 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 327 $a4.4. 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 327 $aDynamic 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 330 $aThis book explains the principles of knowledge structuration to ensure a valid BN and DBN model and illustrate the flexibility and efficiency of these representations in dependability, risk analysis and control of multi-state systems and dynamic systems. Across five chapters, the authors present several modeling methods and industrial applications are referenced for illustration in real industrial contexts.--$cSource other than Library of Congress. 606 $aUncertainty (Information theory)$xMathematical models 606 $aBayesian statistical decision theory 606 $aComputer software$xDevelopment 615 0$aUncertainty (Information theory)$xMathematical models. 615 0$aBayesian statistical decision theory. 615 0$aComputer software$xDevelopment. 676 $a519.5/42 700 $aWeber$b Philippe$0273550 702 $aSimon$b Christophe 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910818395603321 996 $aBenefits of Bayesian network models$93967950 997 $aUNINA