Modeling And Analysis Of Dependable Systems A Probabilistic Graphical Model Perspective
Download Modeling And Analysis Of Dependable Systems A Probabilistic Graphical Model Perspective PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Modeling And Analysis Of Dependable Systems A Probabilistic Graphical Model Perspective book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Modeling And Analysis Of Dependable Systems: A Probabilistic Graphical Model Perspective
The monographic volume addresses, in a systematic and comprehensive way, the state-of-the-art dependability (reliability, availability, risk and safety, security) of systems, using the Artificial Intelligence framework of Probabilistic Graphical Models (PGM). After a survey about the main concepts and methodologies adopted in dependability analysis, the book discusses the main features of PGM formalisms (like Bayesian and Decision Networks) and the advantages, both in terms of modeling and analysis, with respect to classical formalisms and model languages.Methodologies for deriving PGMs from standard dependability formalisms will be introduced, by pointing out tools able to support such a process. Several case studies will be presented and analyzed to support the suitability of the use of PGMs in the study of dependable systems.
Reliability and Availability Engineering
Author: Kishor S. Trivedi
language: en
Publisher: Cambridge University Press
Release Date: 2017-08-03
Learn about the techniques used for evaluating the reliability and availability of engineered systems with this comprehensive guide.
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 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.