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Bayesian methods for the physical sciences[electronic resource] :learning from examples in astronomy and physics /

纪录类型:	书目-电子资源 : Monograph/item
[NT 15000414] null:	519.542
[NT 47271] Title/Author:	Bayesian methods for the physical sciences : learning from examples in astronomy and physics // by Stefano Andreon, Brian Weaver.
作者:	Andreon, Stefano.
[NT 51406] other author:	Weaver, Brian.
出版者:	Cham : : Springer International Publishing :, 2015.
面页册数:	xi, 238 p. : : ill. (some col.), digital ;; 24 cm.
Contained By:	Springer eBooks
标题:	Bayesian statistical decision theory.
标题:	Mathematical physics.
标题:	Statistical astronomy.
标题:	Statistics.
标题:	Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
标题:	Astronomy, Astrophysics and Cosmology.
标题:	Mathematical Methods in Physics.
ISBN:	9783319152875 (electronic bk.)
ISBN:	9783319152868 (paper)
[NT 15000228] null:	Recipes -- A Bit of Theory -- A Bit of Numerical Computation -- Single Parameter Models -- The Prior -- Multi-parameters Models -- Non-random Data Collection -- Fitting Regression Models -- Model Checking and Sensitivity Analysis -- Bayesian vs Simple Methods -- Appendix: Probability Distributions -- Appendix: The third axiom of probability, conditional probability, independence and conditional independence.
[NT 15000229] null:	Statistical literacy is critical for the modern researcher in Physics and Astronomy. This book empowers researchers in these disciplines by providing the tools they will need to analyze their own data. Chapters in this book provide a statistical base from which to approach new problems, including numerical advice and a profusion of examples. The examples are engaging analyses of real-world problems taken from modern astronomical research. The examples are intended to be starting points for readers as they learn to approach their own data and research questions. Acknowledging that scientific progress now hinges on the availability of data and the possibility to improve previous analyses, data and code are distributed throughout the book. The JAGS symbolic language used throughout the book makes it easy to perform Bayesian analysis and is particularly valuable as readers may use it in a myriad of scenarios through slight modifications. This book is comprehensive, well written, and will surely be regarded as a standard text in both astrostatistics and physical statistics. Joseph M. Hilbe, President, International Astrostatistics Association, Professor Emeritus, University of Hawaii, and Adjunct Professor of Statistics, Arizona State University.
电子资源:	http://dx.doi.org/10.1007/978-3-319-15287-5

Bayesian methods for the physical sciences[electronic resource] :learning from examples in astronomy and physics /
Andreon, Stefano.

Bayesian methods for the physical scienceslearning from examples in astronomy and physics /[electronic resource] :by Stefano Andreon, Brian Weaver. - Cham :Springer International Publishing :2015. - xi, 238 p. :ill. (some col.), digital ;24 cm. - Springer series in astrostatistics,2199-1030. - Springer series in astrostatistics..

Recipes -- A Bit of Theory -- A Bit of Numerical Computation -- Single Parameter Models -- The Prior -- Multi-parameters Models -- Non-random Data Collection -- Fitting Regression Models -- Model Checking and Sensitivity Analysis -- Bayesian vs Simple Methods -- Appendix: Probability Distributions -- Appendix: The third axiom of probability, conditional probability, independence and conditional independence.

Statistical literacy is critical for the modern researcher in Physics and Astronomy. This book empowers researchers in these disciplines by providing the tools they will need to analyze their own data. Chapters in this book provide a statistical base from which to approach new problems, including numerical advice and a profusion of examples. The examples are engaging analyses of real-world problems taken from modern astronomical research. The examples are intended to be starting points for readers as they learn to approach their own data and research questions. Acknowledging that scientific progress now hinges on the availability of data and the possibility to improve previous analyses, data and code are distributed throughout the book. The JAGS symbolic language used throughout the book makes it easy to perform Bayesian analysis and is particularly valuable as readers may use it in a myriad of scenarios through slight modifications. This book is comprehensive, well written, and will surely be regarded as a standard text in both astrostatistics and physical statistics. Joseph M. Hilbe, President, International Astrostatistics Association, Professor Emeritus, University of Hawaii, and Adjunct Professor of Statistics, Arizona State University.

ISBN: 9783319152875 (electronic bk.)

Standard No.: 10.1007/978-3-319-15287-5doiSubjects--Topical Terms:

367145
Bayesian statistical decision theory.

LC Class. No.: QC20.7.B38

Dewey Class. No.: 519.542

Bayesian methods for the physical sciences[electronic resource] :learning from examples in astronomy and physics /
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