Weighing the Odds
A Course in Probability and Statistics
$101.00 (X)
- Author: David Williams, University of Wales, Swansea
- Date Published: September 2001
- availability: Available
- format: Paperback
- isbn: 9780521006187
$
101.00
(X)
Paperback
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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.
Read more- Many fundamental concepts from statistics are treated; probability is treated as probabilists think of the subject. Modern computing methods are used throughout and code often provided
- Difficulties in statistics are faced honestly, ample discussion of so-called paradoxes
- Quantum probability and quantum computing are treated in some depth
Reviews & endorsements
"This book presents most of the topics usually found in a full-year sequence on probability and mathematical statistics." The American Statistician
See more reviews"This well-written, interesting, and very useful book provides a lively look and interactions between mathematics, statistics, and probability...An excellent book. Highly recommended." CHOICE
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×Product details
- Date Published: September 2001
- format: Paperback
- isbn: 9780521006187
- length: 568 pages
- dimensions: 247 x 176 x 30 mm
- weight: 1.13kg
- contains: 35 tables 225 exercises
- availability: Available
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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