| Bayesian Logical Data Analysis for the Physical Sciences: A Comparative Approach with Mathematica Support Contributor(s): Gregory, P. C. (Author) |
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ISBN: 052184150X ISBN-13: 9780521841504 Publisher: Cambridge University Press OUR PRICE: $148.20 Product Type: Hardcover - Other Formats Published: May 2005 Annotation: Researchers in many branches of science are increasingly coming into contact with Bayesian statistics or Bayesian probability theory. This book provides a clear exposition of the underlying concepts with large numbers of worked examples and problem sets. It also discusses numerical techniques for implementing the Bayesian calculations, including Markov Chain Monte-Carlo integration and linear and nonlinear least-squares analysis seen from a Bayesian perspective. |
| Additional Information |
| BISAC Categories: - Mathematics | Probability & Statistics - Bayesian Analysis |
| Dewey: 519.542 |
| LCCN: 2004045930 |
| Physical Information: 1.08" H x 6.96" W x 9.96" (2.51 lbs) 468 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: 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(R) notebooks with solutions to selected problems, additional worked examples, and a Mathematica tutorial are available at www.cambridge.org/9780521150125. |