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The Probability Companion for Engineering and Computer Science

The Probability Companion for Engineering and Computer Science

c.$59.99 ( )

  • Publication planned for: December 2019
  • availability: Not yet published - available from December 2019
  • format: Paperback
  • isbn: 9781108727709

c.$ 59.99 ( )
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About the Authors
  • This friendly guide is the companion you need to convert pure mathematics into understanding and facility with a host of probabilistic tools. The book provides a high-level view of probability and its most powerful applications. It begins with the basic rules of probability and quickly progresses to some of the most sophisticated modern techniques in use, including Kalman filters, Monte Carlo techniques, machine learning methods, Bayesian inference and stochastic processes. It draws on thirty years of experience in applying probabilistic methods to problems in computational science and engineering, and numerous practical examples illustrate where these techniques are used in the real world. Topics of discussion range from carbon dating to Wasserstein GANs, one of the most recent developments in Deep Learning. The underlying mathematics is presented in full, but clarity takes priority over complete rigour, making this text a starting reference source for researchers and a readable overview for students.

    • Some sixty-four exercises and ninety-one worked examples feature real-world scenarios
    • Hundreds of diagrams illustrate concepts and results to help readers visualise concepts and improve intuition
    • Detailed mathematical derivations are built for clarity rather than complete rigour
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    Reviews & endorsements

    'In addition to the usual topics of probability theory, a large portion of the book is devoted to presenting modern applications including Bayesian inference and MCMC. Students will appreciate the detailed derivations of formulas and the full solutions of problems. The text is interspersed with personal viewpoints and advice, which gives the book the flavour of a lively lecture by an enthusiastic teacher.' Robert Piché, Tampereen yliopisto, Finland

    'Adam Prügel-Bennett has created a great toolbox for all scientists working with models that take into account the uncertainty of the real world.' Wolfram Burgard, Albert-Ludwigs-Universität Freiburg, Germany

    'This is a wonderful book, one that I wish I'd had when learning about probability. Indeed, there are lots of gems in there that I'm looking forward to reading about myself! The book is beautifully illustrated and refreshingly full of insight, without overly formal mathematical jargon. This book would appeal to students and researchers that are competent in mathematics and delight in gaining a deeper understanding of the subject, both from an intuitive and mathematical standpoint. It excels in demonstrating the wide applicability of probabilistic approaches to problem solving and modelling. This book deserves to be on the shelf of any researcher that uses probability to solve problems.' David Barber, University College London

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    Product details

    • Publication planned for: December 2019
    • format: Paperback
    • isbn: 9781108727709
    • dimensions: 253 x 177 mm
    • availability: Not yet published - available from December 2019
  • Table of Contents

    1. Introduction
    2. Survey of distributions
    3. Monte Carlo
    4. Discrete random variables
    5. The normal distribution
    6. Handling experimental data
    7. Mathematics of random variables
    8. Bayes
    9. Entropy
    10. Collective behavior
    11. Markov chains
    12. Stochastic processes
    Appendix A. Answers to exercises
    Appendix B. Probability distributions.

  • Author

    Adam Prügel-Bennett, University of Southampton
    Adam Prügel-Bennett is Professor of Electronics and Computer Science at the University of Southampton. He received his Ph.D. in Statistical Physics at the University of Edinburgh, where he became interested in disordered and complex systems. He currently researches in the area of mathematical modelling, optimisation and machine learning and has published many papers on these subjects.

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