LEADER 03585nam 2200445 450 001 9910830524903321 005 20230124202112.0 010 $a1-119-53698-7 010 $a1-5231-3321-X 010 $a1-119-53696-0 010 $a1-119-53692-8 035 $a(CKB)4940000000150420 035 $a(MiAaPQ)EBC5990015 035 $a(OCoLC)1134770019 035 $a(CaSebORM)9781119536949 035 $a(EXLCZ)994940000000150420 100 $a20200106d2020 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aProbability with R /$fJane M Horgan 205 $a2nd edition 210 1$aHoboken, New Jersey :$cWiley,$d[2020] 210 4$dİ2020 215 $a1 online resource (499 pages) 311 $a1-119-53694-4 330 $aProvides a comprehensive introduction to probability with an emphasis on computing-related applications 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. Chapters cover: The R language; summarizing statistical data; graphical displays; the fundamentals of probability; reliability; discrete and continuous distributions; and more. This second edition includes: improved R code throughout the text, as well as new procedures, packages and interfaces; updated and additional examples, exercises and projects covering recent developments of computing; 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; an introduction to linear regression with particular emphasis on its application to machine learning using testing and training data; a new section on spam filtering using Bayes theorem to develop the filters; an extended range of Poisson applications such as network failures, website hits, virus attacks and accessing the cloud; use of new allocation functions in R to deal with hash table collision, server overload and the general allocation problem. The book is supplemented with a Wiley Book Companion Site featuring data and solutions to exercises within the book. Primarily addressed to students of computer science and related areas, Probability with R: An Introduction with Computer Science Applications, Second Edition 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. 606 $aComputer science$xMathematics 615 0$aComputer science$xMathematics. 676 $a004.0151 700 $aHorgan$b Jane M.$f1947-$0772200 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910830524903321 996 $aProbability with R$91576311 997 $aUNINA