Bayesian Logical Data Analysis For The Physical Sciences

Author : Phil Gregory
Publisher : Cambridge University Press
Page : 0 pages
File Size : 50,7 Mb
Release : 2010-05-20
Category : Mathematics
ISBN : 0521150124

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Bayesian Logical Data Analysis for the Physical Sciences by Phil Gregory Pdf

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
Page : 498 pages
File Size : 52,5 Mb
Release : 2005-04-14
Category : Mathematics
ISBN : 052184150X

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Bayesian Logical Data Analysis for the Physical Sciences by Philip Christopher Gregory Pdf

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Author : Phil Gregory
Publisher : Cambridge University Press
Page : 498 pages
File Size : 45,7 Mb
Release : 2005-04-14
Category : Mathematics
ISBN : 9781139444286

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Bayesian Logical Data Analysis for the Physical Sciences by Phil Gregory Pdf

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 : Andrew Gelman,John B. Carlin,Hal S. Stern,David B. Dunson,Aki Vehtari,Donald B. Rubin
Publisher : CRC Press
Page : 663 pages
File Size : 47,9 Mb
Release : 2013-11-27
Category : Mathematics
ISBN : 9781439898208

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Bayesian Data Analysis by Andrew Gelman,John B. Carlin,Hal S. Stern,David B. Dunson,Aki Vehtari,Donald B. Rubin Pdf

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 : Devinderjit Sivia,John Skilling
Publisher : OUP Oxford
Page : 264 pages
File Size : 46,6 Mb
Release : 2006-06-02
Category : Mathematics
ISBN : 9780191546709

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Data Analysis by Devinderjit Sivia,John Skilling Pdf

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 : Anonim
Publisher : Unknown
Page : 128 pages
File Size : 49,8 Mb
Release : 2005*
Category : Bayesian statistical decision theory
ISBN : OCLC:176050214

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Bayesian Logical Data Analysis for the Physical Sciences by Anonim Pdf

Author : Edward L. Robinson
Publisher : Princeton University Press
Page : 408 pages
File Size : 46,6 Mb
Release : 2016-10-04
Category : Science
ISBN : 9780691169927

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Data Analysis for Scientists and Engineers by Edward L. Robinson Pdf

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 : Wolfgang von der Linden,Volker Dose,Udo von Toussaint
Publisher : Cambridge University Press
Page : 653 pages
File Size : 47,6 Mb
Release : 2014-06-12
Category : Mathematics
ISBN : 9781107035904

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Bayesian Probability Theory by Wolfgang von der Linden,Volker Dose,Udo von Toussaint Pdf

Covering all aspects of probability theory, statistics and data analysis from a Bayesian perspective for graduate students and researchers.

Author : Giulio D'Agostini
Publisher : World Scientific
Page : 352 pages
File Size : 42,8 Mb
Release : 2003-06-13
Category : Mathematics
ISBN : 9789814486095

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Bayesian Reasoning in Data Analysis by Giulio D'Agostini Pdf

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
Page : 719 pages
File Size : 47,5 Mb
Release : 2017-10-05
Category : Language Arts & Disciplines
ISBN : 9781108416788

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Data Analysis Techniques for Physical Scientists by Claude A. Pruneau Pdf

A comprehensive guide to data analysis techniques for the physical sciences including probability, statistics, data reconstruction, data correction and Monte Carlo methods. This book provides a valuable resource for advanced undergraduate and graduate students, as well as practitioners in the fields of experimental particle physics, nuclear physics and astrophysics.

Author : Lê Nguyên Hoang
Publisher : CRC Press
Page : 461 pages
File Size : 43,8 Mb
Release : 2020-06-18
Category : Mathematics
ISBN : 9781000063233

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The Equation of Knowledge by Lê Nguyên Hoang Pdf

The Equation of Knowledge: From Bayes' Rule to a Unified Philosophy of Science introduces readers to the Bayesian approach to science: teasing out the link between probability and knowledge. The author strives to make this book accessible to a very broad audience, suitable for professionals, students, and academics, as well as the enthusiastic amateur scientist/mathematician. This book also shows how Bayesianism sheds new light on nearly all areas of knowledge, from philosophy to mathematics, science and engineering, but also law, politics and everyday decision-making. Bayesian thinking is an important topic for research, which has seen dramatic progress in the recent years, and has a significant role to play in the understanding and development of AI and Machine Learning, among many other things. This book seeks to act as a tool for proselytising the benefits and limits of Bayesianism to a wider public. Features Presents the Bayesian approach as a unifying scientific method for a wide range of topics Suitable for a broad audience, including professionals, students, and academics Provides a more accessible, philosophical introduction to the subject that is offered elsewhere

Author : Richard McElreath
Publisher : CRC Press
Page : 488 pages
File Size : 50,9 Mb
Release : 2018-01-03
Category : Mathematics
ISBN : 9781315362618

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Statistical Rethinking by Richard McElreath Pdf

Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling. Web Resource The book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.

Author : Coryn A. L. Bailer-Jones
Publisher : Cambridge University Press
Page : 306 pages
File Size : 52,7 Mb
Release : 2017-04-27
Category : Mathematics
ISBN : 9781107192119

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Practical Bayesian Inference by Coryn A. L. Bailer-Jones Pdf

This book introduces the major concepts of probability and statistics, along with the necessary computational tools, for undergraduates and graduate students.

Author : Adrian Bevan
Publisher : Cambridge University Press
Page : 233 pages
File Size : 49,5 Mb
Release : 2013-05-09
Category : Science
ISBN : 9781107067592

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Statistical Data Analysis for the Physical Sciences by Adrian Bevan Pdf

Data analysis lies at the heart of every experimental science. Providing a modern introduction to statistics, this book is ideal for undergraduates in physics. It introduces the necessary tools required to analyse data from experiments across a range of areas, making it a valuable resource for students. In addition to covering the basic topics, the book also takes in advanced and modern subjects, such as neural networks, decision trees, fitting techniques and issues concerning limit or interval setting. Worked examples and case studies illustrate the techniques presented, and end-of-chapter exercises help test the reader's understanding of the material.

Author : John Kruschke
Publisher : Academic Press
Page : 673 pages
File Size : 40,8 Mb
Release : 2010-11-25
Category : Mathematics
ISBN : 9780123814869

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Doing Bayesian Data Analysis by John Kruschke Pdf

There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis tractable and accessible to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS, is for first year graduate students or advanced undergraduates and provides an accessible approach, as all mathematics is explained intuitively and with concrete examples. It assumes only algebra and ‘rusty’ calculus. Unlike other textbooks, this book begins with the basics, including essential concepts of probability and random sampling. The book gradually climbs all the way to advanced hierarchical modeling methods for realistic data. The text provides complete examples with the R programming language and BUGS software (both freeware), and begins with basic programming examples, working up gradually to complete programs for complex analyses and presentation graphics. These templates can be easily adapted for a large variety of students and their own research needs.The textbook bridges the students from their undergraduate training into modern Bayesian methods. Accessible, including the basics of essential concepts of probability and random sampling Examples with R programming language and BUGS software Comprehensive coverage of all scenarios addressed by non-bayesian textbooks- t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis). Coverage of experiment planning R and BUGS computer programming code on website Exercises have explicit purposes and guidelines for accomplishment