{"product_id":"bayesian-logical-data-analysis-for-the-physical-sciences-a-comparative-approach-with-mathematica®-support-hardback-9780521841504","title":"Bayesian Logical Data Analysis for the Physical Sciences; A Comparative Approach with Mathematica® Support (Hardback) 9780521841504","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eBayesian Logical Data Analysis for the Physical Sciences\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eA Comparative Approach with Mathematica® Support\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cem\u003eA clear exposition of the underlying concepts, containing large numbers of worked examples and problem sets, first published in 2005.\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003ePhil Gregory (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780521841504, Cambridge University Press\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 14 April 2005\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e488 pages, 132 b\/w illus.  74 exercises\u003cbr\u003e25.4 x 17.8 x 2.7 cm, 1.06 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e'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\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eBayesian 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.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePreface\u003cbr\u003e Acknowledgements\u003cbr\u003e 1. Role of probability theory in science\u003cbr\u003e 2. Probability theory as extended logic\u003cbr\u003e 3. The how-to of Bayesian inference\u003cbr\u003e 4. Assigning probabilities\u003cbr\u003e 5. Frequentist statistical inference\u003cbr\u003e 6. What is a statistic?\u003cbr\u003e 7. Frequentist hypothesis testing\u003cbr\u003e 8. Maximum entropy probabilities\u003cbr\u003e 9. Bayesian inference (Gaussian errors)\u003cbr\u003e 10. Linear model fitting (Gaussian errors)\u003cbr\u003e 11. Nonlinear model fitting\u003cbr\u003e 12. Markov Chain Monte Carlo\u003cbr\u003e 13. Bayesian spectral analysis\u003cbr\u003e 14. Bayesian inference (Poisson sampling)\u003cbr\u003e Appendix A. Singular value decomposition\u003cbr\u003e Appendix B. Discrete Fourier transforms\u003cbr\u003e Appendix C. Difference in two samples\u003cbr\u003e Appendix D. Poisson ON\/OFF details\u003cbr\u003e Appendix E. Multivariate Gaussian from maximum entropy\u003cbr\u003e References\u003cbr\u003e Index.\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Theoretical \u0026amp; mathematical astronomy [\u003ca title=\"See our other books on Theoretical \u0026amp; mathematical astronomy\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Theoretical%20\u0026amp;%20mathematical%20astronomy%20%5BPGC%5D%22\"\u003ePGC\u003c\/a\u003e], Probability \u0026amp; statistics [\u003ca title=\"See our other books on Probability \u0026amp; statistics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Probability%20\u0026amp;%20statistics%20%5BPBT%5D%22\"\u003ePBT\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":46006065824024,"sku":"9780521841504","price":101.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780521841504i_f0c416d9-7658-41c8-b936-30cfd737cdd9.jpg?v=1691368709","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/bayesian-logical-data-analysis-for-the-physical-sciences-a-comparative-approach-with-mathematica%c2%ae-support-hardback-9780521841504","provider":"Freshly Printed Books","version":"1.0","type":"link"}