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.Additional ISBNs1108480535, 1108573738, 1108727700, 9781108480536, 9781108573733, 9781108727709The Probability Companion for Engineering and Computer Science is written by Adam Prügel-Bennett and published by Cambridge University Press. ISBNs for The Probability Companion for Engineering and Computer Science are 9781108575348, 110857534X and the print ISBNs are 9781108480536, 1108480535. Additional ISBNs include 1108480535, 1108573738, 1108727700, 9781108480536, 9781108573733, 9781108727709.