The Probability Companion for Engineering and Computer Science
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.
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.
Using examples and building
intuition, this friendly guide helps readers understand and use probabilistic tools from basic to sophisticated.