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Bayesian logical data analysis for the physical sciences :a comparative approach with Mathematica support /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	519.5/42
書名/作者:	Bayesian logical data analysis for the physical sciences : : a comparative approach with Mathematica support // P.C. Gregory.
作者:	Gregory, P. C.
面頁冊數:	1 online resource (xvii, 468 pages) : : digital, PDF file(s).
附註:	Title from publisher's bibliographic system (viewed on 05 Oct 2015).
標題:	Bayesian statistical decision theory.
標題:	Physical sciences - Statistical methods.
ISBN:	9780511791277 (ebook)
摘要、提要註:	Bayesian inference provides a simple and unified approach to data analysis, allowing experimenters to assign probabilities to competing hypotheses of interest, on the basis of the current state of knowledge. By incorporating relevant prior information, it can sometimes improve model parameter estimates by many orders of magnitude. This book provides a clear exposition of the underlying concepts with many worked examples and problem sets. It also discusses implementation, including an introduction to Markov chain Monte-Carlo integration and linear and nonlinear model fitting. Particularly extensive coverage of spectral analysis (detecting and measuring periodic signals) includes a self-contained introduction to Fourier and discrete Fourier methods. There is a chapter devoted to Bayesian inference with Poisson sampling, and three chapters on frequentist methods help to bridge the gap between the frequentist and Bayesian approaches. Supporting Mathematica® notebooks with solutions to selected problems, additional worked examples, and a Mathematica tutorial are available at www.cambridge.org/9780521150125.
電子資源:	http://dx.doi.org/10.1017/CBO9780511791277

Bayesian logical data analysis for the physical sciences :a comparative approach with Mathematica support /
Gregory, P. C.1941-

Bayesian logical data analysis for the physical sciences :a comparative approach with Mathematica support /P.C. Gregory. - 1 online resource (xvii, 468 pages) :digital, PDF file(s).

Title from publisher's bibliographic system (viewed on 05 Oct 2015).

Bayesian inference provides a simple and unified approach to data analysis, allowing experimenters to assign probabilities to competing hypotheses of interest, on the basis of the current state of knowledge. By incorporating relevant prior information, it can sometimes improve model parameter estimates by many orders of magnitude. This book provides a clear exposition of the underlying concepts with many worked examples and problem sets. It also discusses implementation, including an introduction to Markov chain Monte-Carlo integration and linear and nonlinear model fitting. Particularly extensive coverage of spectral analysis (detecting and measuring periodic signals) includes a self-contained introduction to Fourier and discrete Fourier methods. There is a chapter devoted to Bayesian inference with Poisson sampling, and three chapters on frequentist methods help to bridge the gap between the frequentist and Bayesian approaches. Supporting Mathematica® notebooks with solutions to selected problems, additional worked examples, and a Mathematica tutorial are available at www.cambridge.org/9780521150125.

ISBN: 9780511791277 (ebook)Subjects--Uniform Titles:

Mathematica (Computer file)
Subjects--Topical Terms:

367145
Bayesian statistical decision theory.

LC Class. No.: QA279.5 / .G74 2005

Dewey Class. No.: 519.5/42

Bayesian logical data analysis for the physical sciences :a comparative approach with Mathematica support /
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