Bayesian Inference for Stochastic Processes (PDF)
(Sprache: Englisch)
The book aims to introduce Bayesian inference methods for stochastic processes. The Bayesian approach has advantages compared to non-Bayesian, among which is the optimal use of prior information via data from previous similar experiments. Examples from...
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The book aims to introduce Bayesian inference methods for stochastic processes. The Bayesian approach has advantages compared to non-Bayesian, among which is the optimal use of prior information via data from previous similar experiments. Examples from biology, economics, and astronomy reinforce the basic concepts of the subject. R and WinBUGS.
Autoren-Porträt von Lyle D. Broemeling
Lyle D. Broemeling, Ph.D., is Director of Broemeling and Associates Inc., and is a consulting biostatistician. He has been involved with academic health science centers for about 20 years and has taught and been a consultant at the University of Texas Medical Branch in Galveston, The University of Texas MD Anderson Cancer Center and the University of Texas School of Public Health. His main interest is in developing Bayesian methods for use in medical and biological problems and in authoring textbooks in statistics. His previous books are Bayesian Biostatistics and Diagnostic Medicine, and Bayesian Methods for Agreement
Bibliographische Angaben
- Autor: Lyle D. Broemeling
- 2017, 448 Seiten, Englisch
- Verlag: Taylor & Francis
- ISBN-10: 1315303582
- ISBN-13: 9781315303581
- Erscheinungsdatum: 12.12.2017
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- Größe: 10 MB
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