## Probability with R: An Introduction with Computer Science Applications (2nd Edition) – eBook

$12.99

## eBook details

**Author:**Jane M. Horgan**File Size:**12 MB**Format:**PDF**Length:**496 pages**Publisher:**Wiley; 2nd edition**Publication Date:**December 18, 2019**Language:**English**ASIN:**B08316D8BN**ISBN-10:**1119536944**ISBN-13:**9781119536949

## Description

* Probability with R: An Introduction with Computer Science Applications 2e* provides a comprehensive introduction to probability with an emphasis on computing-related applications

This self-contained new and extended * Probability with R: An Introduction with Computer Science Applications 2nd edition (PDF)* outlines the first course in probability applied to computer-related disciplines. As in the first edition, experimentation and simulation are favored 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 * 2nd edition* includes:

- a new section on spam filtering using Bayes theorem to develop the filters;
- improved R code throughout the textbook, as well as new procedures, packages, and interfaces;
- updated and additional examples, exercises and projects covering recent developments of computing;
- 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;
- an introduction to linear regression with particular emphasis on its application to machine learning using testing and training data;
- 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.

Primarily addressed to computer science and related students, the ** Probability with R: An Introduction with Computer Science Applications, 2nd Edition **is also an excellent textbook for all students of engineering and the general sciences. Computing professionals who need to understand the relevance of probability in their areas of practice will find it useful.

**NOTE: This sale only contains the ebook Probability with R: An Introduction with Computer Science Applications, 2nd Edition in PDF. No access codes or other media included.**