Book description
Provides a comprehensive introduction to probability with an emphasis on computingrelated applications
This selfcontained new and extended edition outlines a first course in probability applied to computerrelated disciplines. As in the first edition, experimentation and simulation are favoured over mathematical proofs. The freely downloadable 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.
The book is supplemented with a Wiley Book Companion Site featuring data and solutions to exercises within the book.
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.
Table of contents
 Cover
 Preface to the Second Edition
 Preface to the First Edition
 Acknowledgments
 About the Companion Website
 Part I: The R Language

Part II: Fundamentals of Probability

4 Probability Basics
 4.1 Experiments, Sample Spaces, and Events
 4.2 Classical Approach to Probability
 4.3 Permutations and Combinations
 4.4 The Birthday Problem
 4.5 Balls and Bins
 4.6 R Functions for Allocation
 4.7 Allocation Overload
 4.8 Relative Frequency Approach to Probability
 4.9 Simulating Probabilities
 4.10 Projects
 Recommended Reading
 5 Rules of Probability
 6 Conditional Probability
 7 Posterior Probability and Bayes
 8 Reliability

4 Probability Basics

Part III: Discrete Distributions

9 Introduction to Discrete Distributions
 9.1 Discrete Random Variables
 9.2 Cumulative Distribution Function
 9.3 Some Simple Discrete Distributions
 9.4 Benford's Law
 9.5 Summarizing Random Variables: Expectation
 9.6 Properties of Expectations
 9.7 Simulating Discrete Random Variables and Expectations
 9.8 Bivariate Distributions
 9.9 Marginal Distributions
 9.10 Conditional Distributions
 9.11 Project
 References
 10 The Geometric Distribution
 11 The Binomial Distribution
 12 The Hypergeometric Distribution

13 The Poisson Distribution
 13.1 Death by Horse Kick
 13.2 Limiting Binomial Distribution
 13.3 Random Events in Time and Space
 13.4 Probability Density Function
 13.5 Cumulative Distribution Function
 13.6 The Quantile Function
 13.7 Estimating Software Reliability
 13.8 Modeling Defects In Integrated Circuits
 13.9 Simulating Poisson Probabilities
 13.10 Projects
 References

14 Sampling Inspection Schemes
 14.1 Introduction
 14.2 Single Sampling Inspection Schemes
 14.3 Acceptance Probabilities
 14.4 Simulating Sampling Inspection Schemes
 14.5 Operating Characteristic Curve
 14.6 Producer's and Consumer's Risks
 14.7 Design of Sampling Schemes
 14.8 Rectifying Sampling Inspection Schemes
 14.9 Average Outgoing Quality
 14.10 Double Sampling Inspection Schemes
 14.11 Average Sample Size
 14.12 Single Versus Double Schemes
 14.13 Projects

9 Introduction to Discrete Distributions
 Part IV: Continuous Distributions
 Part V: Tailing Off
 Appendix A: Data: Examination Results
 Appendix B: The Line of Best Fit: Coefficient Derivations
 Appendix C: Variance Derivations
 Appendix D: Binomial Approximation to the Hypergeometric
 Appendix E: Normal Tables
 Appendix F: The Inequalities of Markov and Chebyshev
 Index to R Commands
 Index
 Postface
 End User License Agreement
Product information
 Title: Probability with R, 2nd Edition
 Author(s):
 Release date: January 2020
 Publisher(s): Wiley
 ISBN: 9781119536949
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