Bayesian Logical Data Analysis for the Physical Sciences: A Comparative Appro

Description: About this productProduct InformationBayesian 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.Product IdentifiersPublisherCambridge University PressISBN-100521150124ISBN-139780521150125eBay Product ID (ePID)127435618Product Key FeaturesAuthorPhil GregoryPublication NameBayesian Logical Data Analysis for the Physical Sciences : A Comparative Approach with Mathematicar SupportFormatTrade PaperbackLanguageEnglishPublication Year2010TypeTextbookNumber of Pages488 PagesDimensionsItem Length9.6in.Item Height1.1in.Item Width6.7in.Item Weight27.3 OzAdditional Product FeaturesLc Classification NumberQa279.5.G74 2010Reviews"The book can easily keep the readers amazed and attracted to its content throughout the read and make them want to return back to it recursively. It presents a perfect balance between theoretical inference and a practical know-how approach to Bayesian methods." Stan Lipovetsky, GfK Custom Research North America, Technometrics, 'The book can easily keep the readers amazed and attracted to its content throughout the read and make them want to return back to it recursively. It presents a perfect balance between theoretical inference and a practical know-how approach to Bayesian methods.' Stan Lipovetsky, Technometrics, 'As well as the usual topics to be found in a text on Bayesian inference, chapters are included on frequentist inference (for contrast), non-linear model fitting, spectral analysis and Poisson sampling.' Zentralblatt MATH, "All researchers and scientists who are interested in the Bayesian scientific paradigm can benefit greatly from the examples and illustrations here. It is a welcome addition to the vast literature on Bayesian inference." Sreenivasan Ravi, University of Mysore, ManasagangotriTable of ContentPreface; Acknowledgements; 1. Role of probability theory in science; 2. Probability theory as extended logic; 3. The how-to of Bayesian inference; 4. Assigning probabilities; 5. Frequentist statistical inference; 6. What is a statistic?; 7. Frequentist hypothesis testing; 8. Maximum entropy probabilities; 9. Bayesian inference (Gaussian errors); 10. Linear model fitting (Gaussian errors); 11. Nonlinear model fitting; 12. Markov Chain Monte Carlo; 13. Bayesian spectral analysis; 14. Bayesian inference (Poisson sampling); Appendix A. Singular value decomposition; Appendix B. Discrete Fourier transforms; Appendix C. Difference in two samples; Appendix D. Poisson ON/OFF details; Appendix E. Multivariate Gaussian from maximum entropy; References; Index.Copyright Date2010Target AudienceScholarly & ProfessionalTopicMathematical & Statistical Software, Probability & Statistics / General, General, Probability & Statistics / Bayesian AnalysisDewey Decimal519.542Dewey Edition22IllustratedYesGenreComputers, Science, Mathematics

Price: 45.9 USD

Location: Multiple Locations

End Time: 2024-11-09T12:32:12.000Z

Shipping Cost: 3.97 USD