Bayesian Logical Data Analysis For The Physical Sciences

Author : Phil Gregory
Publisher : Cambridge University Press
ISBN 13 : 9780521150125
Total Pages : 0 pages
Book Rating : 4.24/5 ( download)

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Download or read book Bayesian Logical Data Analysis for the Physical Sciences written by Phil Gregory and published by Cambridge University Press. This book was released on 2010-05-20 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Author : Philip Christopher Gregory
Publisher : Cambridge University Press
ISBN 13 : 9780521841504
Total Pages : 498 pages
Book Rating : 4.0X/5 ( download)

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Download or read book Bayesian Logical Data Analysis for the Physical Sciences written by Philip Christopher Gregory and published by Cambridge University Press. This book was released on 2005-04-14 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: Publisher Description

Author : Phil Gregory
Publisher : Cambridge University Press
ISBN 13 : 113944428X
Total Pages : 498 pages
Book Rating : 4.86/5 ( download)

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Download or read book Bayesian Logical Data Analysis for the Physical Sciences written by Phil Gregory and published by Cambridge University Press. This book was released on 2005-04-14 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

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Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.14/5 ( download)

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Download or read book Bayesian Logical Data Analysis for the Physical Sciences written by and published by . This book was released on 2005* with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Author : Andrew Gelman
Publisher : CRC Press
ISBN 13 : 1439898200
Total Pages : 663 pages
Book Rating : 4.08/5 ( download)

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Download or read book Bayesian Data Analysis written by Andrew Gelman and published by CRC Press. This book was released on 2013-11-27 with total page 663 pages. Available in PDF, EPUB and Kindle. Book excerpt: Winner of the 2016 De Groot Prize from the International Society for Bayesian AnalysisNow in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied

Author : Wolfgang von der Linden
Publisher : Cambridge University Press
ISBN 13 : 1107035902
Total Pages : 653 pages
Book Rating : 4.04/5 ( download)

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Download or read book Bayesian Probability Theory written by Wolfgang von der Linden and published by Cambridge University Press. This book was released on 2014-06-12 with total page 653 pages. Available in PDF, EPUB and Kindle. Book excerpt: Covering all aspects of probability theory, statistics and data analysis from a Bayesian perspective for graduate students and researchers.

Author : Devinderjit Sivia
Publisher : OUP Oxford
ISBN 13 : 0191546704
Total Pages : 264 pages
Book Rating : 4.09/5 ( download)

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Download or read book Data Analysis written by Devinderjit Sivia and published by OUP Oxford. This book was released on 2006-06-02 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the strengths of this book is the author's ability to motivate the use of Bayesian methods through simple yet effective examples. - Katie St. Clair MAA Reviews.

Author : Edward L. Robinson
Publisher : Princeton University Press
ISBN 13 : 0691169926
Total Pages : 408 pages
Book Rating : 4.27/5 ( download)

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Download or read book Data Analysis for Scientists and Engineers written by Edward L. Robinson and published by Princeton University Press. This book was released on 2016-10-04 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Analysis for Scientists and Engineers is a modern, graduate-level text on data analysis techniques for physical science and engineering students as well as working scientists and engineers. Edward Robinson emphasizes the principles behind various techniques so that practitioners can adapt them to their own problems, or develop new techniques when necessary. Robinson divides the book into three sections. The first section covers basic concepts in probability and includes a chapter on Monte Carlo methods with an extended discussion of Markov chain Monte Carlo sampling. The second section introduces statistics and then develops tools for fitting models to data, comparing and contrasting techniques from both frequentist and Bayesian perspectives. The final section is devoted to methods for analyzing sequences of data, such as correlation functions, periodograms, and image reconstruction. While it goes beyond elementary statistics, the text is self-contained and accessible to readers from a wide variety of backgrounds. Specialized mathematical topics are included in an appendix. Based on a graduate course on data analysis that the author has taught for many years, and couched in the looser, workaday language of scientists and engineers who wrestle directly with data, this book is ideal for courses on data analysis and a valuable resource for students, instructors, and practitioners in the physical sciences and engineering. In-depth discussion of data analysis for scientists and engineers Coverage of both frequentist and Bayesian approaches to data analysis Extensive look at analysis techniques for time-series data and images Detailed exploration of linear and nonlinear modeling of data Emphasis on error analysis Instructor's manual (available only to professors)

Author : Giulio D'Agostini
Publisher : World Scientific
ISBN 13 : 9814486094
Total Pages : 352 pages
Book Rating : 4.95/5 ( download)

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Download or read book Bayesian Reasoning in Data Analysis written by Giulio D'Agostini and published by World Scientific. This book was released on 2003-06-13 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a multi-level introduction to Bayesian reasoning (as opposed to “conventional statistics”) and its applications to data analysis. The basic ideas of this “new” approach to the quantification of uncertainty are presented using examples from research and everyday life. Applications covered include: parametric inference; combination of results; treatment of uncertainty due to systematic errors and background; comparison of hypotheses; unfolding of experimental distributions; upper/lower bounds in frontier-type measurements. Approximate methods for routine use are derived and are shown often to coincide — under well-defined assumptions! — with “standard” methods, which can therefore be seen as special cases of the more general Bayesian methods. In dealing with uncertainty in measurements, modern metrological ideas are utilized, including the ISO classification of uncertainty into type A and type B. These are shown to fit well into the Bayesian framework. Contents: Critical Review and Outline of the Bayesian Alternative:Uncertainty in Physics and the Usual Methods of Handling ItA Probabilistic Theory of Measurement UncertaintyA Bayesian Primer:Subjective Probability and Bayes' TheoremProbability Distributions (A Concise Reminder)Bayesian Inference of Continuous QuantitiesGaussian LikelihoodCounting ExperimentsBypassing Bayes' Theorem for Routine ApplicationsBayesian UnfoldingFurther Comments, Examples and Applications:Miscellanea on General Issues in Probability and InferenceCombination of Experimental Results: A Closer LookAsymmetric Uncertainties and Nonlinear PropagationWhich Priors for Frontier Physics?Conclusion:Conclusions and Bibliography Readership: Graduate students and researchers interested in probability and statistics and their applications in science, particularly the evaluation of uncertainty in measurements. Keywords:Probability;Bayesian Statistics;Error Theory;Measurement Uncertainty;MetrologyReviews:“… statistics textbooks must take seriously the need to teach the foundations of statistical reasoning from the beginning … D'Agostini's new book does this admirably, building an edifice of Bayesian statistical reasoning in the physical sciences on solid foundations.”Journal of the American Statistical Association

Author : Claude A. Pruneau
Publisher : Cambridge University Press
ISBN 13 : 1108267882
Total Pages : 719 pages
Book Rating : 4.85/5 ( download)

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Download or read book Data Analysis Techniques for Physical Scientists written by Claude A. Pruneau and published by Cambridge University Press. This book was released on 2017-10-05 with total page 719 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive guide to data analysis techniques for physical scientists, providing a valuable resource for advanced undergraduate and graduate students, as well as seasoned researchers. The book begins with an extensive discussion of the foundational concepts and methods of probability and statistics under both the frequentist and Bayesian interpretations of probability. It next presents basic concepts and techniques used for measurements of particle production cross-sections, correlation functions, and particle identification. Much attention is devoted to notions of statistical and systematic errors, beginning with intuitive discussions and progressively introducing the more formal concepts of confidence intervals, credible range, and hypothesis testing. The book also includes an in-depth discussion of the methods used to unfold or correct data for instrumental effects associated with measurement and process noise as well as particle and event losses, before ending with a presentation of elementary Monte Carlo techniques.