Probability with R: An Introduction with Computer Science Applications (2nd Edition) – eBook

Description

Probability with R: An Introduction with Computer Science Applications, 2nd Edition offers an extensive exploration of probability with a focus on computing-related applications, making it an ideal resource for students and professionals alike.

This newly revised and enriched Probability with R: An Introduction with Computer Science Applications, 2nd Edition (PDF) serves as a foundational course in probability tailored for those engaged in computer science and related fields. Unlike traditional textbooks that lean heavily on mathematical proofs, this edition emphasizes hands-on experimentation and simulation. Throughout the text, the open-source statistical programming language R is utilized not just for numerical calculations and data analysis, but also to vividly illustrate fundamental concepts of probability and demonstrate the simulation of various distributions. The book’s practical examples encompass a variety of computer science applications, including performance testing of software programs, assessing response and CPU processing times, estimating the reliability of both components and systems, and analyzing algorithms as well as queuing phenomena.

In-depth chapters cover a spectrum of vital topics, including but not limited to: The R language essentials, efficient summarization of statistical data, graphical data representations, the core principles of probability theory, reliability analysis, and various discrete and continuous probability distributions.

This enhanced 2nd Edition features:

• A dedicated section on spam filtering using Bayes’ theorem, providing practical insights into filter development;
• Refined R code throughout the book, along with the introduction of novel procedures, packages, and interfaces;
• Updated examples, exercises, and projects that reflect the latest advancements in computing technologies;
• An expanded discussion on Poisson applications, addressing critical issues like network failures, website traffic, virus attacks, and cloud access;
• Innovative allocation functions in R to manage hash table collisions, server overloads, and general allocation challenges;
• A comprehensive introduction to linear regression, highlighting its applicability in machine learning through the lens of training and testing datasets;
• An overview of bivariate discrete distributions, along with the R functions designed to manipulate large matrices of conditional probabilities, crucial for tasks such as machine translation.

Targeted primarily at students in computer science and related disciplines, the Probability with R: An Introduction with Computer Science Applications, 2nd Edition also serves as an invaluable textbook for engineering students and others in the general sciences. Computing professionals seeking to comprehend the significance of probability in their respective fields will find this resource particularly beneficial.

NOTE: This listing is for the ebook Probability with R: An Introduction with Computer Science Applications, 2nd Edition, in PDF format. Please note that access codes or any supplementary media are not included.