Benefits of Bayesian Network Models

Nonfiction, Science & Nature, Mathematics
Cover of the book Benefits of Bayesian Network Models by Philippe Weber, Christophe Simon, Wiley
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Author: Philippe Weber, Christophe Simon ISBN: 9781119347446
Publisher: Wiley Publication: August 23, 2016
Imprint: Wiley-ISTE Language: English
Author: Philippe Weber, Christophe Simon
ISBN: 9781119347446
Publisher: Wiley
Publication: August 23, 2016
Imprint: Wiley-ISTE
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

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