Jane M. Horgan
Wiley; 2nd edition
An Introduction to Probability Using R, with Emphasis on Its Applications in Computer Science The second edition offers a comprehensive introduction to probability, with an emphasis on applications that are connected to computers.
This self-contained revised and extended version of Probability with R: An Introduction with Computer Science Applications 2nd edition (PDF) describes the first course in probability applied to computer-related fields of study. Experimentation and simulation continue to take precedence over mathematical proofs, just as they did in the original edition. The statistical computer language R, which can be downloaded for free, is used throughout the entire book in a variety of contexts. Not only is it utilized as a tool for calculation and data analysis, but it is also used to teach topics related to probability and to model distributions. The examples in the second edition of Probability with R: An Introduction with Computer Science Applications cover a wide variety of computer science applications. These applications include testing the performance of programs, measuring response time and CPU time, estimating the reliability of components and systems, and evaluating algorithms and queuing systems.
The R programming language, summarizing statistical data, graphical displays, the principles of probability, reliability, discrete and continuous distributions, and more are all covered in detail throughout the chapters of this book.
This second edition contains the following:
- a whole new chapter on spam filtering that makes use of Bayes' theorem to create the filters;
- enhanced R code throughout the entirety of the textbook, in addition to brand new procedures, packages, and interfaces
- examples that have been updated and expanded; activities and initiatives that cover contemporary advances in computer technology;
- an extensive variety of applications of the Poisson distribution, including but not limited to network failures, website hits, virus attacks, and accessing the cloud;
- using R's new allocation methods to address issues such as collisions in hash tables, server overload, and the overall allocation problem;
- an introduction to linear regression, with a focus on its use in machine learning through the utilization of testing and training data;
- an introduction to bivariate discrete distributions together with the R functions necessary to handle big matrices of conditional probabilities; this is something that is frequently required in machine translation.
The Probability with R: An Introduction with Computer Science Applications; 2nd Edition is a great textbook for all students of engineering and the general sciences, despite the fact that its primary audience is students studying computer science and fields closely related to it. Professionals in the field of computing who need to grasp the importance of probability in their fields of activity will find that this resource is valuable.
PLEASE TAKE NOTICE That the only thing included in this deal is the PDF version of the ebook Probability with R: An Introduction with Computer Science Applications, 2nd Edition. There are no access codes or other material included in this purchase.
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