國立虎尾科技大學 |

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

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Bayesian logical data analysis for the physical sciences :/ P.C. Gregory.
Reminder of title:	a comparative approach with Mathematica support /
Author:	Gregory, P. C.
Description:	1 online resource (xvii, 468 pages) :digital, PDF file(s). :
Notes:	Title from publisher's bibliographic system (viewed on 05 Oct 2015).
Subject:	Bayesian statistical decision theory. -
Online resource:	https://doi.org/10.1017/CBO9780511791277
ISBN:	9780511791277 (ebook)

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:

527671
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 /
LDR:02175nam a2200289 i 4500 001 1122440
003 UkCbUP
005 20160418132058.0
006 m|||||o||d||||||||
007 cr||||||||||||
008 240926s2005||||enk o ||1 0|eng|d
020 $a 9780511791277 (ebook)
020 $z 9780521841504 (hardback)
020 $z 9780521150125 (paperback)
035 $a CR9780511791277
040 $a UkCbUP $b eng $e rda $c UkCbUP
050 0 0 $a QA279.5 $b .G74 2005
082 0 0 $a 519.5/42 $2 22
100 1 $a Gregory, P. C. $q (Philip Christopher), $d 1941- $e author. $3 1438638
245 1 0 $a Bayesian logical data analysis for the physical sciences : $b a comparative approach with Mathematica support / $c P.C. Gregory.
264 1 $a Cambridge : $b Cambridge University Press, $c 2005.
300 $a 1 online resource (xvii, 468 pages) : $b digital, PDF file(s).
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Title from publisher's bibliographic system (viewed on 05 Oct 2015).
520 $a 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.
630 0 0 $a Mathematica (Computer file) $3 591363
650 0 $a Bayesian statistical decision theory. $3 527671
650 0 $a Physical sciences $x Statistical methods. $3 908553
776 0 8 $i Print version: $z 9780521841504
856 4 0 $u https://doi.org/10.1017/CBO9780511791277