Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics (Statistics for Biology and Health)

Gianola, Daniel,Sorensen, Daniel

ISBN 10: 047132048X / ISBN 13: 9780471320487

Published by Springer, 2002

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Bibliographic Details

• Title: Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics (Statistics for Biology and Health)
• Publisher: Springer
• Publication Date: 2002
• ISBN 10: 047132048X
• ISBN 13: 9780471320487
• Binding: Hardcover
• Condition: Good

About this title

Synopsis

Model-based fault detection and isolation requires a mathematical model of the system behaviour. Modelling is important and can be difficult because of the complexity of the monitored system and its control architecture. The authors use bond-graph modelling, a unified multi-energy domain modelling method, to build dynamic models of process engineering systems by composing hierarchically arranged sub-models of various commonly encountered process engineering devices. The structural and causal properties of bond-graph models are exploited for supervisory systems design. The structural properties of a system, necessary for process control, are elegantly derived from bond-graph models by following the simple algorithms presented here. Additionally, structural analysis of the model augmented with available instrumentation indicates directly whether it is possible to detect and/or isolate faults in some specific sub-space of the process. Such analysis aids in the design and resource optimiza

About the Author

JIE CHEN, PhD, is Professor of Electrical Engineering at the University of California, Riverside. GUOXIANG GU, PhD, is Professor in the Department of Electrical and Computer Engineering at Louisiana State University, Baton Rouge.