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| 001 | 9780429488443 | ||
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| 008 | 190418s2019 flu ob 000 0 eng d | ||
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_a9780429488443 _q(electronic bk.) |
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_a9780429948909 _q(electronic bk. : Mobipocket) |
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_a9780429948923 _q(electronic bk. : PDF) |
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| 020 | _z9781138591523 | ||
| 020 | _z1138591521 | ||
| 035 | _a(OCoLC)1097611865 | ||
| 035 | _a(OCoLC-P)1097611865 | ||
| 050 | 4 |
_aQA280 _b.B765 2019eb |
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_aMAT _x003000 _2bisacsh |
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_aMAT _x029000 _2bisacsh |
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_a519.5/5 _223 |
| 100 | 1 |
_aBroemeling, Lyle D., _d1939- _eauthor. |
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| 245 | 1 | 0 |
_aBayesian analysis of time series _h[electronic resource] / _cLyle D. Broemeling. |
| 264 | 1 |
_aBoca Raton : _bCRC Press, Taylor & Francis Group, _c2019. |
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| 300 | _a1 online resource | ||
| 520 | _aIn 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. | ||
| 588 | _aOCLC-licensed vendor bibliographic record. | ||
| 650 | 0 |
_aTime-series analysis _vTextbooks. |
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| 650 | 0 |
_aBayesian statistical decision theory _vTextbooks. |
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| 650 | 7 |
_aMATHEMATICS / Applied. _2bisacsh |
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| 650 | 7 |
_aMATHEMATICS / Probability & Statistics / General. _2bisacsh |
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| 650 | 7 |
_aREFERENCE / General _2bisacsh |
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| 856 | 4 | 0 |
_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9780429488443 |
| 856 | 4 | 2 |
_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf |
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