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

Gianola, Daniel,Sorensen, Daniel

ISBN 10: 047132048X / ISBN 13: 9780471320487

Editore: Springer, 2002

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Codice articolo S_397871975

Dati bibliografici

• Titolo: Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics (Statistics for Biology and Health)
• Editore: Springer
• Data di pubblicazione: 2002
• ISBN 10: 047132048X
• ISBN 13: 9780471320487
• Rilegatura: Hardcover
• Condizione: Good

Descrizione articolo

Riassunto

A comprehensive, one-stop reference for new system modeling and identification tools

The field of control-oriented identification has grown immensely over the past decade, spawning numerous results and modeling techniques and promising the potential to influence science and engineering for years to come. In this new work, Jie Chen and Guoxiang Gu, two leading authorities on worst-case identification, share their vision and walk readers through carefully selected topics from the vast literature, offering a much-needed, timely comprehensive introduction to the theory of H identification and model validation.

Chen and Gu clearly demonstrate the pros and cons of the worst-case approach in comparison to traditional techniques and provide researchers in systems and control theory with ready access to many new and complementary identification tools. Through a rigorous yet logical and easy-to-follow treatment, supported by many deep insights, intuitions, and philosophical thinking, they:
* Survey and assess the current state of control and system identification research
* Develop both two-stage and interpolatory algorithms for system identification
* Show readers how to analyze the properties of linear algorithms
* Offer a unique emphasis on model uncertainty estimation and complexity, two of the central issues
* Develop both time-domain and frequency-domain identification algorithms
* Explain in detail uncertainty model validation concepts and techniques
* Devote a chapter to a review of the requisite mathematics


Provide a concise yet self-contained appendix on several key relevant notions

Informazioni sull?autore

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.