Bayesian Logical Data Analysis for the Physical Sciences

Please use this identifier to cite or link to this item: 192.168.6.56/handle/123456789/17277

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DC Field	Value	Language
dc.contributor.editor	Gregory, P. C.	-
dc.date.accessioned	2018-11-05T05:47:12Z	-
dc.date.available	2018-11-05T05:47:12Z	-
dc.date.issued	2005	-
dc.identifier.isbn	978-0-511-08228-3	-
dc.identifier.uri	http://10.6.20.12:80/handle/123456789/17277	-
dc.description	This book 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.	-
dc.language.iso	en	en_US
dc.publisher	Cambridge University Press	en_US
dc.subject	Data Analysis	en_US
dc.title	Bayesian Logical Data Analysis for the Physical Sciences	en_US
dc.title.alternative	A Comparative Approach with Mathematica Support	en_US
dc.type	Book	en_US
Appears in Collections:	Medical Labratory

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