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E-grāmata: Benefits of Bayesian Network Models

  • Formāts: PDF+DRM
  • Izdošanas datums: 23-Aug-2016
  • Izdevniecība: ISTE Ltd and John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781119347453
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  • Formāts: PDF+DRM
  • Izdošanas datums: 23-Aug-2016
  • Izdevniecība: ISTE Ltd and John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781119347453
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The application of Bayesian Networks (BR) in dependability is a relatively recent development. Their popularity grew in the area of reliability analysis of systems, since the 1990s. A large number of scientific publications in this area show the interest in the applications of BN in the field of dependability and risk analysis. The most important publications demonstrate equivalence with probabilistic methods conventionally used in dependability. We have now a number of survey papers that gives a good view of the ability of BN application to dependability. Unfortunately, this modeling formalism is not fully accepted in the industry. The questions posed by today's engineers focus on the validity of BN models and the resulting estimates. Indeed, the modeling formalism by BN 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 model and illustrate the flexibility and efficiency of representation by PGMs.

Foreword ix
J. F. Aubry
Foreword xiii
L. Portinale
Acknowledgments xv
Introduction xvii
Part 1 Bayesian Networks
1(64)
Chapter 1 Bayesian Networks: a Modeling Formalism for System Dependability
3(14)
1.1 Probabilistic graphical models: BN
5(3)
1.1.1 BN: a formalism to model dependability
5(2)
1.1.2 Inference mechanism
7(1)
1.2 Reliability and joint probability distributions
8(6)
1.2.1 Multi-state system example
8(1)
1.2.2 Joint distribution
9(1)
1.2.3 Reliability computing
9(1)
1.2.4 Factorization
10(4)
1.3 Discussion and conclusion
14(3)
Chapter 2 Bayesian Network: Modeling Formalism of the Stucture Function of Boolean Systems
17(26)
2.1 Introduction
17(2)
2.2 BN models in the Boolean case
19(10)
2.2.1 BN model from cut-sets
20(3)
2.2.2 BN model from tie-sets
23(2)
2.2.3 BN model from a top-down approach
25(1)
2.2.4 BN model of a bowtie
26(3)
2.3 Standard Boolean gates CPT
29(2)
2.4 Non-deterministic CPT
31(7)
2.5 Industrial applications
38(3)
2.6 Conclusion
41(2)
Chapter 3 Bayesian Network: Modeling Formalism of the Structure Function of Multi-State Systems
43(22)
3.1 Introduction
43(1)
3.2 BN models in the multi-state case
43(15)
3.2.1 BN model of multi-state systems from tie-sets
44(5)
3.2.2 BN model of multi-state systems from cut-sets
49(3)
3.2.3 BN model of multi-state systems from functional and dysfunctional analysis
52(6)
3.3 Non-deterministic CPT
58(1)
3.4 Industrial applications
59(3)
3.5 Conclusion
62(3)
Part 2 Dynamic Bayesian Networks
65(32)
Chapter 4 Dynamic Bayesian Networks: Integrating Environmental and Operating Constraints in Reliability Computation
67(16)
4.1 Introduction
67(2)
4.2 Component modeled by a DBN
69(6)
4.2.1 DBN model of a MC
70(1)
4.2.2 DBN model of non-homogeneous MC
71(1)
4.2.3 Stochastic process with exogenous constraint
72(3)
4.3 Model of a dynamic multi-state system
75(4)
4.4 Discussion on dependent processes
79(2)
4.5 Conclusion
81(2)
Chapter 5 Dynamic Bayesian Networks: Integrating Reliability Computation in the Control System
83(14)
5.1 Introduction
83(1)
5.2 Integrating reliability information into the control
84(1)
5.3 Control integrating reliability modeled by DBN
85(5)
5.3.1 Modeling and controlling an over-actuated system
86(2)
5.3.2 Integrating reliability
88(2)
5.4 Application to a drinking water network
90(5)
5.4.1 DBN modeling
91(1)
5.4.2 Results and discussion
92(3)
5.5 Conclusion
95(1)
5.6 Acknowledgments
96(1)
Conclusion 97(4)
Bibliography 101(12)
Index 113
Philippe Weber is Professor at the Engineer School of Sciences and Technologies at the University of Lorraine and at the Research Centre for Automatic Control in Nancy, France. His research concerns dependability and is mainly focused on probabilistic graphical models. Christophe Simon is Associate Professor at the Research Centre for Automatic Control in Nancy, France. His research concerns dependability and is mainly focused on modeling engineering and uncertainties.