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Weighing the Odds
A Course in Probability and Statistics

$259.00 (X)

  • Date Published: September 2001
  • availability: Available
  • format: Hardback
  • isbn: 9780521803564

$ 259.00 (X)

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About the Authors
  • 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.

    • 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
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    Reviews & endorsements

    "This book presents most of the topics usually found in a full-year sequence on probability and mathematical statistics." The American Statistician

    "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: Hardback
    • isbn: 9780521803564
    • length: 566 pages
    • dimensions: 244 x 170 x 32 mm
    • weight: 1.1kg
    • contains: 35 tables 225 exercises
    • availability: Available
  • Table of Contents

    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

  • Author

    David Williams, University of Wales, Swansea

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