English | 2020 | ISBN: 978-1119536949 | 496 Pages | PDF, EPUB | 30 MBProvides a comprehensive introduction to probability with an emphasis on computing-related applications This self-contained new and extended edition outlines a first course in probability applied to computer-related disciplines. As in the first edition, experimentation and simulation are favoured over mathematical proofs. The freely down-loadable statistical programming language R is used throughout the text, not only as a tool for calculation and data analysis, but also to illustrate concepts of probability and to simulate distributions. The examples in Probability with R: An Introduction with Computer Science Applications, Second Edition cover a wide range of computer science applications, including: testing program performance; measuring response time and CPU time; estimating the reliability of components and systems; evaluating algorithms and queuing systems. Chapters cover: The R language; summarizing statistical data; graphical displays; the fundamentals of probability; reliability; discrete and continuous distributions; and more. This second edition includes:
- improved R code throughout the text, as well as new procedures, packages and interfaces;
- updated and additional examples, exercises and projects covering recent developments of computing;
- an introduction to bivariate discrete distributions together with the R functions used to handle large matrices of conditional probabilities, which are often needed in machine translation;
- an introduction to linear regression with particular emphasis on its application to machine learning using testing and training data;
- a new section on spam filtering using Bayes theorem to develop the filters;
- an extended range of Poisson applications such as network failures, website hits, virus attacks and accessing the cloud;
- use of new allocation functions in R to deal with hash table collision, server overload and the general allocation problem.