{"product_id":"probability-with-r-an-introduction-with-computer-science-applications-hardcover","title":"Probability with R: An Introduction with Computer Science Applications - Hardcover","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eJane M. Horgan\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eProvides a comprehensive introduction to probability with an emphasis on computing-related applications\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThis 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 \u003ci\u003eR \u003c\/i\u003eis 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 \u003ci\u003eProbability with R: An Introduction with Computer Science Applications, Second Edition \u003c\/i\u003ecover 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. \u003c\/p\u003e \u003cp\u003eChapters cover: The R language; summarizing statistical data; graphical displays; the fundamentals of probability; reliability; discrete and continuous distributions; and more. \u003c\/p\u003e \u003cp\u003eThis second edition includes: \u003c\/p\u003e \u003cul\u003e \u003cli\u003eimproved R code throughout the text, as well as new procedures, packages and interfaces;\u003c\/li\u003e \u003cli\u003eupdated and additional examples, exercises and projects covering recent developments of computing;\u003c\/li\u003e \u003cli\u003ean 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;\u003c\/li\u003e \u003cli\u003ean introduction to linear regression with particular emphasis on its application to machine learning using testing and training data;\u003c\/li\u003e \u003cli\u003ea new section on spam filtering using Bayes theorem to develop the filters;\u003c\/li\u003e \u003cli\u003ean extended range of Poisson applications such as network failures, website hits, virus attacks and accessing the cloud;\u003c\/li\u003e \u003cli\u003euse of new allocation functions in R to deal with hash table collision, server overload and the general allocation problem.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThe book is supplemented with a Wiley Book Companion Site featuring data and solutions to exercises within the book.\u003c\/p\u003e \u003cp\u003ePrimarily addressed to students of computer science and related areas, \u003ci\u003eProbability with R: An Introduction with Computer Science Applications, Second Edition \u003c\/i\u003eis 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.\u003c\/p\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eProvides a comprehensive introduction to probability with an emphasis on computing-related applications\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eThis 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 downloadable statistical programming language \u003ci\u003eR\u003c\/i\u003e 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 \u003ci\u003eProbability with R: An Introduction with Computer Science Applications, Second Edition\u003c\/i\u003e cover a wide range of computer science applications, including: testing program performance; measuring response time and CPU time; training and testing in machine learning; estimating the reliability of components and systems; evaluating algorithms and queuing systems. \u003c\/p\u003e\u003cp\u003eChapters cover: The R language; summarizing statistical data; graphical displays; the fundamentals of probability; reliability; discrete and continuous distributions; and more. \u003c\/p\u003e\u003cp\u003eThis second edition includes: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eimproved R code throughout the text, as well as new procedures, packages and interfaces;\u003c\/li\u003e \u003cli\u003eupdated and additional examples, exercises and projects covering recent developments in computing;\u003c\/li\u003e \u003cli\u003ean 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;\u003c\/li\u003e \u003cli\u003ean introduction to linear regression with particular emphasis on its application to machine learning using testing and training data;\u003c\/li\u003e \u003cli\u003ea new section on spam filtering using Bayes theorem to develop the filters;\u003c\/li\u003e \u003cli\u003ean extended range of Poisson applications such as network failures, website hits, virus attacks and accessing the cloud;\u003c\/li\u003e \u003cli\u003euse of new allocation functions in R to deal with hash table collision, server overload and the general allocation problem.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThe book is supplemented with a Wiley Instructor-only Book Companion Site featuring data and solutions to exercises within the book. \u003c\/p\u003e\u003cp\u003ePrimarily addressed to students of computer science and related areas, \u003ci\u003eProbability with R: An Introduction with Computer Science Applications, Second Edition\u003c\/i\u003e 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.\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eJANE M. HORGAN\u003c\/b\u003e is Emeritus Professor of Statistics in the School of Computing, Dublin City University, Ireland. A Fellow of the Institute of Statisticians, she graduated in Statistics with a First Class Honours from University College Cork and completed postgraduate work at the London School of Economics and at London City University. Dr. Horgan has published extensively in statistics and computing.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 496\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1.2 x 9.1 x 6.2 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e January 22, 2020\u003c\/div\u003e\n            ","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":43159578214463,"sku":"9781119536949","price":210.73,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0105\/8226\/1823\/files\/B3jO46bzdU9781119536949.webp?v=1776999867","url":"https:\/\/dhlswag.com\/products\/probability-with-r-an-introduction-with-computer-science-applications-hardcover","provider":"BBB","version":"1.0","type":"link"}