The Probability Companion for Engineering and Computer Science

Erscheinungsjahr 2020
ISBN: 978-1-108-72770-9
Verlag: Cambridge University Press

Buch, Englisch, 436 Seiten, Format (B × H): 180 mm x 249 mm, Gewicht: 998 g

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.

Autoren/Hrsg.

Prügel-Bennett, Adam

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik
• Mathematik | Informatik Mathematik Stochastik
• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Ökonometrie
• Wirtschaftswissenschaften Finanzsektor & Finanzdienstleistungen Versicherungswirtschaft

Inhaltsverzeichnis

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.

Über Autor(innen)

Prügel-Bennett, Adam
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.