$56.00 (0) USD
Adobe eBook Reader
Looking for an examination copy?
If you are interested in the title for your course we can consider offering an examination copy. To register your interest please contact firstname.lastname@example.org providing details of the course you are teaching.
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.Read more
- Introduces statistical inference in the larger context of scientific methods, and includes many worked examples and problem sets
- Presents Bayesian theory but also compares and contrasts with other existing ideas
- Mathematica® support notebook is available for readers from www.cambridge.org/9780521150125
Reviews & endorsements
"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, ManasagangotriSee more reviews
"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
Not yet reviewed
Be the first to review
Review was not posted due to profanity×
- Date Published: May 2005
- format: Adobe eBook Reader
- isbn: 9780511081385
- contains: 132 b/w illus. 74 exercises
- availability: Adobe Reader ebooks available from eBooks.com
Table of Contents
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
Welcome to the resources site
Here you will find free-of-charge online materials to accompany this book. The range of materials we provide across our academic and higher education titles are an integral part of the book package whether you are a student, instructor, researcher or professional.
Find resources associated with this titleYour search for '' returned .
Type Name Unlocked * Format Size
*This title has one or more locked files and access is given only to instructors adopting the textbook for their class. We need to enforce this strictly so that solutions are not made available to students. To gain access to locked resources you either need first to sign in or register for an account.
These resources are provided free of charge by Cambridge University Press with permission of the author of the corresponding work, but are subject to copyright. You are permitted to view, print and download these resources for your own personal use only, provided any copyright lines on the resources are not removed or altered in any way. Any other use, including but not limited to distribution of the resources in modified form, or via electronic or other media, is strictly prohibited unless you have permission from the author of the corresponding work and provided you give appropriate acknowledgement of the source.
If you are having problems accessing these resources please email email@example.com
Sorry, this resource is locked
Please register or sign in to request access. If you are having problems accessing these resources please email firstname.lastname@example.orgRegister Sign in
You are now leaving the Cambridge University Press website, your eBook purchase and download will be completed by our partner www.ebooks.com. Please see the permission section of the www.ebooks.com catalogue page for details of the print & copy limits on our eBooks.Continue ×