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.
Author(s): Adam Prügel-Bennett
Publisher: Cambridge University Press
Year: 2020
Language: English
Pages: 476
Tags: Engineering: Statistical Methods, Computer Science: Statistical Methods, Probabilities
Cover......Page 1
Front Matter
......Page 3
The Probability Companion for Engineering and
Computer Science......Page 5
Copyright
......Page 6
Contents
......Page 7
Preface
......Page 13
Nomenclature......Page 15
1 Introduction......Page 19
2 Survey of Distributions......Page 43
3 Monte Carlo......Page 63
4 Discrete Random Variables......Page 77
5 The Normal Distribution......Page 92
6 Handling Experimental Data......Page 128
7 Mathematics of Random Variables......Page 150
8 Bayes......Page 205
9 Entropy......Page 276
10 Collective Behaviour......Page 311
11 Markov Chains......Page 326
12 Stochastic Processes......Page 367
Appendix A.
Answers to Exercises......Page 409
Appendix B.
Probability Distributions......Page 463
Bibliography
......Page 467
Index......Page 472
