Probability with R
An Introduction with Computer Science Applications
(Sprache: Englisch)
There is a great need for a book on introductory probability applied to problems in computing. Probability with R serves as an introduction to probability and its application to computer disciplines and successfully convinces readers of the relevance of probability to computing.
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There is a great need for a book on introductory probability applied to problems in computing. Probability with R serves as an introduction to probability and its application to computer disciplines and successfully convinces readers of the relevance of probability to computing.
Klappentext zu „Probability with R “
A Complete Introduction to probability AND its computer Science Applications USING RProbability with R serves as a comprehensive and introductory book on probability with an emphasis on computing-related applications. Real examples show how probability can be used in practical situations, and the freely available and downloadable statistical programming language R illustrates and clarifies the book's main principles.
Promoting a simulation- and experimentation-driven methodology, this book highlights the relationship between probability and computing in five distinctive parts:
* The R Language presents the essentials of the R language, including key procedures for summarizing and building graphical displays of statistical data.
* Fundamentals of Probability provides the foundations of the basic concepts of probability and moves into applications in computing. Topical coverage includes conditional probability, Bayes' theorem, system reliability, and the development of the main laws and properties of probability.
* Discrete Distributions addresses discrete random variables and their density and distribution functions as well as the properties of expectation. The geometric, binomial, hypergeometric, and Poisson distributions are also discussed and used to develop sampling inspection schemes.
* Continuous Distributions introduces continuous variables by examining the waiting time between Poisson occurrences. The exponential distribution and its applications to reliability are investigated, and the Markov property is illustrated via simulation in R. The normal distribution is examined and applied to statistical process control.
* Tailing Off delves into the use of Markov and Chebyshev inequalities as tools for estimating tail probabilities with limited information on the random variable.
Numerous exercises and projects are provided in each chapter, many of which require the use of R to perform routine calculations and conduct experiments
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with simulated data. The author directs readers to the appropriate Web-based resources for installing the R software package and also supplies the essential commands for working in the R workspace. A related Web site features an active appendix as well as a forum for readers to share findings, thoughts, and ideas.
With its accessible and hands-on approach, Probability with R is an ideal book for a first course in probability at the upper-undergraduate and graduate levels for readers with a background in computer science, engineering, and the general sciences. It also serves as a valuable reference for computing professionals who would like to further understand the relevance of probability in their areas of practice.
With its accessible and hands-on approach, Probability with R is an ideal book for a first course in probability at the upper-undergraduate and graduate levels for readers with a background in computer science, engineering, and the general sciences. It also serves as a valuable reference for computing professionals who would like to further understand the relevance of probability in their areas of practice.
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There is a great need for a book on introductory probability applied to problems in computing. Probability with R serves as an introduction to probability and its application to computer disciplines and successfully convinces readers of the relevance of probability to computing. Most examples are related to computing and cover a wide range of computer science applications. This thoroughly classroom-tested, self-contained book encourages computing professionals and upper-undergraduates to perform the simulations in R in order to gain a firm understanding of the concepts discussed in the book.
Inhaltsverzeichnis zu „Probability with R “
Preface.I. THE R LANGUAGE.
1. Basics of R.
2. Summarising Statistical Data.
3. Graphical Displays.
II: FUNDAMENTALS OF PROBABILITY.
4. Basics.
5. Rules of Probability.
6. Conditional Probability.
7. Posterior Probability and Bayes.
8. Reliability.
III: DISCRETE DISTRIBUTIONS.
9. Discrete Distributions.
10. The Geometric Distribution.
11. The Binomial Distribution.
12. The Hypergeometric Distribution.
13. The Poisson Distribution.
14. Sampling Inspection Schemes.
IV. CONTINUOUS DISTRIBUTIONS.
15. Continuous Distributions.
16. The Exponential Distribution.
17. Applications of the Exponential Distribution.
18. The Normal Distribution.
19. Process Control.
V. TAILING OFF.
20. Markov and Chebyshev Bound.
Appendix 1: Variance derivations.
Appendix 2: Binomial approximation to the hypergeometric.
Appendix 3: Standard Normal Tables.
Autoren-Porträt von Jane M. Horgan
Jane M. Horgan is Associate Professor of Statistics in the School of Computing at Dublin City University, Ireland. A Fellow of the Institute of Statisticians, Dr. Horgan has published extensively in the areas of statistical sampling and estimation. Her research interests include applications to both financial auditing and rare incidence skewed populations.
Bibliographische Angaben
- Autor: Jane M. Horgan
- 2008, 1. Auflage, 416 Seiten, Maße: 16,4 x 23,8 cm, Gebunden, Englisch
- Verlag: Wiley & Sons
- ISBN-10: 0470280735
- ISBN-13: 9780470280737
- Erscheinungsdatum: 13.01.2009
Sprache:
Englisch
Pressezitat
"Probability with R: An Introduction with Computer Science Applications addresses an important niche by concurrently providing an introduction to probability for students who have had only the most rudimentary calculus training, and an introduction to the freely available software R for students with no programming experience." ( Journal of Classification, 2010) "For the audience this book is intended, the book is the best lecture notes a teacher can give to students and to anybody wishing to self teach themselves, with that caveat." ( Journal of Statistical Software, Jan 2009) "This is a wonderful text for beginners to learn probability. Reading it is a joy. Although the book is oriented towards CS majors, I believe that students in other fields can also benefit from it." ( Computing Reviews, Jan 2009) "..a great quality of this book is that it could be used by anybody with a reasonable level of education to self teach themselves probability and its applications... It is more user friendly than the R manuals and, because the code is used within the examples, it teaches R for probability much more effectively." ( Journal of Statistical Software, 2008)
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