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The Theory of Probability
Explorations and Applications

$98.00 (P)

  • Date Published: December 2012
  • format: Hardback
  • isbn: 9781107024472

$ 98.00 (P)
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About the Authors
  • From classical foundations to advanced modern theory, this self-contained and comprehensive guide to probability weaves together mathematical proofs, historical context and richly detailed illustrative applications. A theorem discovery approach is used throughout, setting each proof within its historical setting and is accompanied by a consistent emphasis on elementary methods of proof. Each topic is presented in a modular framework, combining fundamental concepts with worked examples, problems and digressions which, although mathematically rigorous, require no specialised or advanced mathematical background. Augmenting this core material are over 80 richly embellished practical applications of probability theory, drawn from a broad spectrum of areas both classical and modern, each tailor-made to illustrate the magnificent scope of the formal results. Providing a solid grounding in practical probability, without sacrificing mathematical rigour or historical richness, this insightful book is a fascinating reference and essential resource, for all engineers, computer scientists and mathematicians.

    • Modular structure, with clearly differentiated core material, applications and digressions, allows for easy development of bespoke study modules and encourages further exploration
    • Self-contained and comprehensive, requiring no specialised or advanced mathematical background
    • Accompanied by over 500 problems, more than 250 worked examples and over 80 highly detailed applications from fields as diverse as computation, epidemiology complexity, neuroscience, astronomy, genetics, prediction, physics, queuing and actuarial science
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    Reviews & endorsements

    "This is a remarkable book, a theory of probability that succeeds in being both readable and rigorous, both expository and entertaining. One might have thought that there was no space left in the market for books on the fundamentals of probability theory, but this volume provides a refreshing new approach … it is a magnificent undertaking, impeccably presented, and one that is sure to reward repeated reading."
    Tom Fanshawe, Significance (magazine of The Royal Statistical Society)

    "… well-written, and although the topics are discussed with all mathematical rigour, it usually does not exceed the capabilities of an advanced undergraduate student … it can be recommended without constraint as a textbook for advanced undergraduates, but also as a reference and interesting read for experts."
    Manuel Vogel, Contemporary Physics

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    Product details

    • Date Published: December 2012
    • format: Hardback
    • isbn: 9781107024472
    • length: 827 pages
    • dimensions: 253 x 179 x 41 mm
    • weight: 1.81kg
    • contains: 100 b/w illus. 26 tables 528 exercises
  • Table of Contents

    Part I. Elements:
    1. Probability spaces
    2. Conditional probability
    3. A first look at independence
    4. Probability sieves
    5. Numbers play a game of chance
    6. The normal law
    7. Probabilities on the real line
    8. The Bernoulli schema
    9. The essence of randomness
    10. The coda of the normal
    Part II. Foundations:
    11. Distribution functions and measure
    12. Random variables
    13. Great expectations
    14. Variations on a theme of integration
    15. Laplace transforms
    16. The law of large numbers
    17. From inequalities to concentration
    18. Poisson approximation
    19. Convergence in law, selection theorems
    20. Normal approximation
    Part III. Appendices:
    21. Sequences, functions, spaces.

  • Resources for

    The Theory of Probability

    Santosh S. Venkatesh

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  • Instructors have used or reviewed this title for the following courses

    • Evolution Theory of Stochastic Processes
    • Probability-Random Signal and Statistics
  • Author

    Santosh S. Venkatesh, University of Pennsylvania
    Santosh S. Venkatesh is an Associate Professor of Electrical and Systems Engineering at the University of Pennsylvania, whose research interests include probability, information, communication and learning theory, pattern recognition, computational neuroscience, epidemiology and computer security. He is a member of the David Mahoney Institute for Neurological Sciences and has been awarded the Lindback Award for Distinguished Teaching.

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