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Bayesian Analysis of Time Series (Hardcover)
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Bayesian Analysis of Time Series (Hardcover)
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In many branches of science relevant observations are taken
sequentially over time. Bayesian Analysis of Time Series discusses
how to use models that explain the probabilistic characteristics of
these time series and then utilizes the Bayesian approach to make
inferences about their parameters. This is done by taking the prior
information and via Bayes theorem implementing Bayesian inferences
of estimation, testing hypotheses, and prediction. The methods are
demonstrated using both R and WinBUGS. The R package is primarily
used to generate observations from a given time series model, while
the WinBUGS packages allows one to perform a posterior analysis
that provides a way to determine the characteristic of the
posterior distribution of the unknown parameters. Features Presents
a comprehensive introduction to the Bayesian analysis of time
series. Gives many examples over a wide variety of fields including
biology, agriculture, business, economics, sociology, and
astronomy. Contains numerous exercises at the end of each chapter
many of which use R and WinBUGS. Can be used in graduate courses in
statistics and biostatistics, but is also appropriate for
researchers, practitioners and consulting statisticians. About the
author 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 for Chapman & Hall/CRC include
Bayesian Biostatistics and Diagnostic Medicine, and Bayesian
Methods for Agreement.
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