Weighing the Odds: A Course in Probability and Statistics / Edition 1 by David Williams | 9780521006187 | Paperback | Barnes & Noble
Weighing the Odds: A Course in Probability and Statistics / Edition 1

Weighing the Odds: A Course in Probability and Statistics / Edition 1

by David Williams
     
 

ISBN-10: 052100618X

ISBN-13: 9780521006187

Pub. Date: 03/28/2010

Publisher: Cambridge University Press

In this lively look at both subjects, David Williams convinces Mathematics students of the intrinsic interest of Statistics and Probability, and Statistics students that the language of Mathematics can bring real insight and clarity to their subject. He helps students build the intuition needed, in a presentation enriched with examples drawn from all manner of

Overview

In this lively look at both subjects, David Williams convinces Mathematics students of the intrinsic interest of Statistics and Probability, and Statistics students that the language of Mathematics can bring real insight and clarity to their subject. He helps students build the intuition needed, in a presentation enriched with examples drawn from all manner of applications. Statistics chapters present both the Frequentist and Bayesian approaches, emphasizing Confidence Intervals rather than Hypothesis Test, and include Gibbs-sampling techniques for the practical implementation of Bayesian methods. A central chapter gives the theory of Linear Regression and ANOVA, and explains how MCMC methods allow greater flexibility in modeling. C or WinBUGS code is provided for computational examples and simulations.

Product Details

ISBN-13:
9780521006187
Publisher:
Cambridge University Press
Publication date:
03/28/2010
Edition description:
New Edition
Pages:
568
Product dimensions:
6.85(w) x 9.72(h) x 1.14(d)

Related Subjects

Table of Contents

Preface; 1. Introduction; 2. Events and probabilities; 3. Random variables, means and variances; 4. Conditioning and independence; 5. Generating functions and the central limit theorem; 6. Confidence intervals for 1-parameter models; 7. Conditional pdfs and multi-parameter Bayesian statistics; 8. Linear models, ANOVA etc; 9. Some further probability; 10. Quantum probability and quantum computing; Appendix A. Some prerequisites and addenda; Appendix B. Discussion of some selected exercises; Appendix C. Tables; Appendix D. A small sample of the literature; Bibliography; Index.

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