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Benefits of Bayesian Network Models

Author Christophe Simon , Philippe Weber
Release Date: 2016/08/01
ISBN: 9781848219922
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Book Description

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.

Table of Contents

  1. Cover
  2. Title
  3. Copyright
  4. Foreword by J.-F. Aubry
  5. Foreword by L. Portinale
  6. Acknowledgments
  7. Introduction
    1. I.1. Problem statement
    2. I.2. Book structure
  8. PART 1: Bayesian Networks
    1. 1 Bayesian Networks: a Modeling Formalism for System Dependability
      1. 1.1. Probabilistic graphical models: BN
      2. 1.2. Reliability and joint probability distributions
      3. 1.3. Discussion and conclusion
    2. 2 Bayesian Network: Modeling Formalism of the Structure Function of Boolean Systems
      1. 2.1. Introduction
      2. 2.2. BN models in the Boolean case
      3. 2.3. Standard Boolean gates CPT
      4. 2.4. Non-deterministic CPT
      5. 2.5. Industrial applications
      6. 2.6. Conclusion
    3. 3 Bayesian Network: Modeling Formalism of the Structure Function of Multi-State Systems
      1. 3.1. Introduction
      2. 3.2. BN models in the multi-state case
      3. 3.3. Non-deterministic CPT
      4. 3.4. Industrial applications
      5. 3.5. Conclusion
  9. PART 2: Dynamic Bayesian Networks
    1. 4 Dynamic Bayesian Networks: Integrating Environmental and Operating Constraints in Reliability Computation
      1. 4.1. Introduction
      2. 4.2. Component modeled by a DBN
      3. 4.3. Model of a dynamic multi-state system
      4. 4.4. Discussion on dependent processes
      5. 4.5. Conclusion
    2. 5 Dynamic Bayesian Networks: Integrating Reliability Computation in the Control System
      1. 5.1. Introduction
      2. 5.2. Integrating reliability information into the control
      3. 5.3. Control integrating reliability modeled by DBN
      4. 5.4. Application to a drinking water network
      5. 5.5. Conclusion
      6. 5.6. Acknowledgments
  10. Conclusion
  11. Bibliography
  12. Index
  13. End User License Agreement
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