# Probability with R: An Introduction with Computer Science Applications, 2nd Edition

PDF, ePUB

- eBook:Probability with R: An Introduction with Computer Science Applications, 2nd Edition
- Author:Jane M. Horgan
- Edition:2 edition
- Categories:
- Data:January 22, 2020
- ISBN:1119536944
- ISBN-13:9781119536949
- Language:English
- Pages:496 pages
- Format:PDF, ePUB

**Book Description**

**Provides 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.

Primarily addressed to students of computer science and related areas,

*Probability with R: An Introduction with Computer Science Applications, Second Edition*is also an excellent text for 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.

**Content**

1. Basics of R

2. Summarizing Statistical Data

3. Graphical Displays

Part II: Fundamentals of Probability

4. Probability Basics

5. Rules of Probability

6. Conditional Probability

7. Posterior Probability and Bayes

8. Reliability

Part III: Discrete Distributions

9. Introduction to Discrete Distributions

10. The Geometric Distribution

11. The Binomial Distribution

12. The Hypergeometric Distribution

13. The Poisson Distribution

14. Sampling Inspection Schemes

Part IV: Continuous Distributions

15. Introduction to Continuous Distributions

16. The Exponential Distribution

17. Queues

18. The Normal Distribution

19. Process Control

Part V: Tailing Off

20. The Inequalities of Markov and Chebyshev

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