Cantitate/Preț
Produs

Bayesian Logical Data Analysis for the Physical Sciences: A Comparative Approach with Mathematica® Support

Autor Phil Gregory
en Limba Engleză Paperback – 19 mai 2010
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.
Citește tot Restrânge

Toate formatele și edițiile

Toate formatele și edițiile Preț Express
Paperback (1) 49704 lei  6-8 săpt.
  Cambridge University Press – 19 mai 2010 49704 lei  6-8 săpt.
Hardback (1) 88752 lei  6-8 săpt.
  Cambridge University Press – 13 apr 2005 88752 lei  6-8 săpt.

Preț: 49704 lei

Preț vechi: 55847 lei
-11% Nou

Puncte Express: 746

Preț estimativ în valută:
9511 9957$ 7870£

Carte tipărită la comandă

Livrare economică 05-19 aprilie

Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9780521150125
ISBN-10: 0521150124
Pagini: 488
Ilustrații: 132 b/w illus. 74 exercises
Dimensiuni: 170 x 244 x 28 mm
Greutate: 0.77 kg
Editura: Cambridge University Press
Colecția Cambridge University Press
Locul publicării:Cambridge, United Kingdom

Cuprins

Preface; 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.

Recenzii

'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
'The examples are well integrated with the text and are enlightening.' Contemporary Physics
'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

Notă biografică


Descriere

A clear exposition of the underlying concepts, containing large numbers of worked examples and problem sets, first published in 2005.