| 000 -LEADER |
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| fixed length control field |
02373nam a2200217Ia 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20241112122505.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
170804s2010 xx 000 0 und d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9780521841504 |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
ICTS-TIFR |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
QA279.5 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Phil Gregory |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Bayesian logical data analysis for the physical sciences |
| Remainder of title |
: a comparative approach with mathematica® support |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Name of publisher, distributor, etc. |
Cambridge University Press, |
| Date of publication, distribution, etc. |
[c2005] |
| Place of publication, distribution, etc. |
Cambridge, U.K.: |
| 300 ## - Physical Description |
|---|
| Pages: |
467 p. |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
1 - Role of probability theory in science <br/>2 - Probability theory as extended logic <br/>3 - The how-to of Bayesian inference<br/>4 - Assigning probabilities <br/>5 - Frequentist statistical inference <br/>6 - What is a statistic? <br/>7 - Frequentist hypothesis testing <br/>8 - Maximum entropy probabilities <br/>9 - Bayesian inference with Gaussian errors <br/>10 - Linear model fitting (Gaussian errors)<br/>11 - Nonlinear model fitting <br/>12 - Markov chain Monte Carlo <br/>13 - Bayesian revolution in spectral analysis <br/>14 - Bayesian inference with Poisson sampling <br/> |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
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.---Provided by publisher |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Statistics and probability |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
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| Uniform Resource Identifier |
<a href="https://assets.cambridge.org/97805218/41504/toc/9780521841504_toc.pdf">https://assets.cambridge.org/97805218/41504/toc/9780521841504_toc.pdf</a> |
| Link text |
Table of Contents |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
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| Koha item type |
Book |
