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Bayesian Analysis of Time Series

Lyle D. Broemeling
€65.99
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Bayes theorem
Bayesian inference for scientific research
Bayesian Posterior Means
Bayesian Predictive Density
bayesian textbooks
Bayesian Time Series
Category=KCH
Category=PBT
Category=PBWL
Credible Interval
Discrete Time Linear Dynamic Systems
Dynamic Linear Model
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Federal Aviation Administration
Gibbs Sequences
graduate statistics textbook
Informative Prior Information
Kalman Filter
linear models
Marginal Posterior Density
multivariate analysis
nonstationary data methods
Posterior Analysis
Posterior Density
Posterior Distribution
Posterior Mass Function
Precision Matrix
Prior Density
probabilistic characteristics
R package
Random Walk Series
Regression analysis
Regression Model
Shift Point
Spectral Density Function
Standardized Residuals
statistical modeling
stochastic processes
Sunspot Data
Tar Model
time series
time series forecasting
WinBUGS Code
WinBUGS package

Product details

  • ISBN 9780367779993
  • Weight: 453g
  • Dimensions: 156 x 234mm
  • Publication Date: 31 Mar 2021
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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Description

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.
About 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 for Chapman & Hall/CRC include Bayesian Biostatistics and Diagnostic Medicine, and Bayesian Methods for Agreement.

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