Cargando…
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, NJ :
Wiley-ISTE,
2016.
|
| Colección: | Systems and industrial engineering series.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
| LEADER | 00000cam a2200000Ma 4500 | ||
|---|---|---|---|
| 001 | EBOOKCENTRAL_ocn957655787 | ||
| 003 | OCoLC | ||
| 005 | 20240329122006.0 | ||
| 006 | m o d | ||
| 007 | cr |n||||||||| | ||
| 008 | 160902s2016 nju ob 001 0 eng d | ||
| 040 | |a IDEBK |b eng |e pn |c IDEBK |d N$T |d NLE |d EBLCP |d DG1 |d RECBK |d N$T |d OCLCF |d DG1 |d OCLCQ |d IDEBK |d UPM |d STF |d DG1 |d YDX |d VGM |d OTZ |d OCLCQ |d CCO |d K6U |d MERUC |d LOA |d COCUF |d ZCU |d ICG |d OCLCQ |d WRM |d KSU |d OCLCQ |d U3W |d OCLCQ |d ESU |d DKC |d OCLCQ |d UKAHL |d OCLCQ |d OCLCO |d OCLCQ |d AKR |d OCLCO |d OCLCL | ||
| 019 | |a 957524986 |a 960029522 |a 960030149 | ||
| 020 | |a 9781119347316 |q (electronic bk.) | ||
| 020 | |a 1119347319 |q (electronic bk.) | ||
| 020 | |a 1119347459 |q (electronic bk.) | ||
| 020 | |a 9781119347453 |q (electronic bk.) | ||
| 020 | |z 184821992X | ||
| 020 | |z 1119347440 | ||
| 020 | |z 9781119347446 | ||
| 020 | |z 9781848219922 | ||
| 029 | 1 | |a AU@ |b 000061349460 | |
| 029 | 1 | |a CHBIS |b 011150587 | |
| 029 | 1 | |a CHBIS |b 011278923 | |
| 029 | 1 | |a CHNEW |b 000945622 | |
| 029 | 1 | |a CHVBK |b 480257825 | |
| 029 | 1 | |a DEBSZ |b 485069342 | |
| 029 | 1 | |a GBVCP |b 1002765803 | |
| 035 | |a (OCoLC)957655787 |z (OCoLC)957524986 |z (OCoLC)960029522 |z (OCoLC)960030149 | ||
| 037 | |a 950696 |b MIL | ||
| 050 | 4 | |a QA279.5 | |
| 072 | 7 | |a MAT |x 003000 |2 bisacsh | |
| 072 | 7 | |a MAT |x 029000 |2 bisacsh | |
| 082 | 0 | 4 | |a 519.5 |2 23 |
| 049 | |a UAMI | ||
| 100 | 1 | |a Weber, Philippe |c (Co-author of Data uncertainty and important measures), |e author. |1 https://id.oclc.org/worldcat/entity/E39PCjtJG7hGF74Xc7WPq7MrRq | |
| 245 | 1 | 0 | |a Benefits of Bayesian network models. |
| 260 | |a Hoboken, NJ : |b Wiley-ISTE, |c 2016. | ||
| 300 | |a 1 online resource (151) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Systems and industrial engineering series | |
| 490 | 1 | |a Systems dependability assessment set ; |v volume 2 | |
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 520 | |a 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 in the industry. The questions facing today's engineers are focused on the validity of BN models and the resulting estimates. Indeed, a BN model is not based on a specific semantic in dependability but offers a general formalism for modeling problems under uncertainty. This 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. 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 in the industry. The questions facing today's engineers are focused on the validity of BN models and the resulting estimates. Indeed, a BN model is not based on a specific semantic in dependability but offers a general formalism for modeling problems under uncertainty. This 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. | ||
| 504 | |a Includes bibliographical references and index. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Bayesian statistical decision theory. | |
| 650 | 0 | |a Mathematical models. | |
| 650 | 2 | |a Models, Theoretical | |
| 650 | 6 | |a Théorie de la décision bayésienne. | |
| 650 | 6 | |a Modèles mathématiques. | |
| 650 | 7 | |a mathematical models. |2 aat | |
| 650 | 7 | |a MATHEMATICS |x General. |2 bisacsh | |
| 650 | 7 | |a MATHEMATICS |x Applied. |2 bisacsh | |
| 650 | 7 | |a MATHEMATICS |x Probability & Statistics |x General. |2 bisacsh | |
| 650 | 7 | |a Bayesian statistical decision theory |2 fast | |
| 650 | 7 | |a Mathematical models |2 fast | |
| 758 | |i has work: |a Benefits of Bayesian networks models (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGQbPTfMWCHQk7mghpTfYK |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 830 | 0 | |a Systems and industrial engineering series. | |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=4658585 |z Texto completo |
| 938 | |a Askews and Holts Library Services |b ASKH |n AH31375488 | ||
| 938 | |a EBL - Ebook Library |b EBLB |n EBL4658585 | ||
| 938 | |a EBSCOhost |b EBSC |n 1342627 | ||
| 938 | |a ProQuest MyiLibrary Digital eBook Collection |b IDEB |n cis35385803 | ||
| 938 | |a Recorded Books, LLC |b RECE |n rbeEB00685850 | ||
| 938 | |a YBP Library Services |b YANK |n 13140085 | ||
| 938 | |a YBP Library Services |b YANK |n 13187441 | ||
| 994 | |a 92 |b IZTAP | ||