Original PDF Ebook – Probability with R2nd EditionAn Introduction with Computer Science Applications – 9781119536949
Provides a comprehensive introduction to probability with an emphasis on computing-related applications_x000D_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. _x000D_
Chapters cover: The R language; summarizing statistical data; graphical displays; the fundamentals of probability; reliability; discrete and continuous distributions; and more. _x000D_
This second edition includes:_x000D_
improved R code throughout the text, as well as new procedures, packages and interfaces;_x000D_
updated and additional examples, exercises and projects covering recent developments of computing;_x000D_
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;_x000D_
an introduction to linear regression with particular emphasis on its application to machine learning using testing and training data;_x000D_
a new section on spam filtering using Bayes theorem to develop the filters;_x000D_
an extended range of Poisson applications such as network failures, website hits, virus attacks and accessing the cloud;_x000D_
use of new allocation functions in R to deal with hash table collision, server overload and the general allocation problem.
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