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.
General
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