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| Author | : Adam Prügel-Bennett |
| Publisher | : Cambridge University Press |
| Total Pages | : 475 |
| Release | : 2020-01-23 |
| Genre | : Business & Economics |
| ISBN | : 1108480535 |
Using examples and building intuition, this friendly guide helps readers understand and use probabilistic tools from basic to sophisticated.
| Author | : Adam Prügel-Bennett |
| Publisher | : Cambridge University Press |
| Total Pages | : 475 |
| Release | : 2020-01-23 |
| Genre | : Mathematics |
| ISBN | : 1108573738 |
This friendly guide is the companion you need to convert pure mathematics into understanding and facility with a host of probabilistic tools. The book provides a high-level view of probability and its most powerful applications. It begins with the basic rules of probability and quickly progresses to some of the most sophisticated modern techniques in use, including Kalman filters, Monte Carlo techniques, machine learning methods, Bayesian inference and stochastic processes. It draws on thirty years of experience in applying probabilistic methods to problems in computational science and engineering, and numerous practical examples illustrate where these techniques are used in the real world. Topics of discussion range from carbon dating to Wasserstein GANs, one of the most recent developments in Deep Learning. The underlying mathematics is presented in full, but clarity takes priority over complete rigour, making this text a starting reference source for researchers and a readable overview for students.
| Author | : Kishor S. Trivedi |
| Publisher | : John Wiley & Sons |
| Total Pages | : 1042 |
| Release | : 2016-06-30 |
| Genre | : Computers |
| ISBN | : 1119314208 |
An accessible introduction to probability, stochastic processes, and statistics for computer science and engineering applications Second edition now also available in Paperback. This updated and revised edition of the popular classic first edition relates fundamental concepts in probability and statistics to the computer sciences and engineering. The author uses Markov chains and other statistical tools to illustrate processes in reliability of computer systems and networks, fault tolerance, and performance. This edition features an entirely new section on stochastic Petri nets—as well as new sections on system availability modeling, wireless system modeling, numerical solution techniques for Markov chains, and software reliability modeling, among other subjects. Extensive revisions take new developments in solution techniques and applications into account and bring this work totally up to date. It includes more than 200 worked examples and self-study exercises for each section. Probability and Statistics with Reliability, Queuing and Computer Science Applications, Second Edition offers a comprehensive introduction to probability, stochastic processes, and statistics for students of computer science, electrical and computer engineering, and applied mathematics. Its wealth of practical examples and up-to-date information makes it an excellent resource for practitioners as well. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.
| Author | : Jean Walrand |
| Publisher | : Springer Nature |
| Total Pages | : 391 |
| Release | : 2021-06-22 |
| Genre | : Computers |
| ISBN | : 3030499952 |
This revised textbook motivates and illustrates the techniques of applied probability by applications in electrical engineering and computer science (EECS). The author presents information processing and communication systems that use algorithms based on probabilistic models and techniques, including web searches, digital links, speech recognition, GPS, route planning, recommendation systems, classification, and estimation. He then explains how these applications work and, along the way, provides the readers with the understanding of the key concepts and methods of applied probability. Python labs enable the readers to experiment and consolidate their understanding. The book includes homework, solutions, and Jupyter notebooks. This edition includes new topics such as Boosting, Multi-armed bandits, statistical tests, social networks, queuing networks, and neural networks. For ancillaries related to this book, including examples of Python demos and also Python labs used in Berkeley, please email Mary James at [email protected]. This is an open access book.
| Author | : Jane M. Horgan |
| Publisher | : John Wiley & Sons |
| Total Pages | : 522 |
| Release | : 2019-12-18 |
| Genre | : Mathematics |
| ISBN | : 1119536987 |
Provides 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.
| Author | : Sheldon M. Ross |
| Publisher | : Taylor & Francis US |
| Total Pages | : 304 |
| Release | : 2002 |
| Genre | : Computers |
| ISBN | : 9780125980517 |
The role of probability in computer science has been growing for years and, in lieu of a tailored textbook, many courses have employed a variety of similar, but not entirely applicable, alternatives. To meet the needs of the computer science graduate student (and the advanced undergraduate), best-selling author Sheldon Ross has developed the premier probability text for aspiring computer scientists involved in computer simulation and modeling. The math is precise and easily understood. As with his other texts, Sheldon Ross presents very clear explanations of concepts and covers those probability models that are most in demand by, and applicable to, computer science and related majors and practitioners. Many interesting examples and exercises have been chosen to illuminate the techniques presented Examples relating to bin packing, sorting algorithms, the find algorithm, random graphs, self-organising list problems, the maximum weighted independent set problem, hashing, probabilistic verification, max SAT problem, queuing networks, distributed workload models, and many othersMany interesting examples and exercises have been chosen to illuminate the techniques presented
| Author | : Michael Baron |
| Publisher | : CRC Press |
| Total Pages | : 427 |
| Release | : 2018-11-14 |
| Genre | : Mathematics |
| ISBN | : 1420011421 |
In modern computer science, software engineering, and other fields, the need arises to make decisions under uncertainty. Presenting probability and statistical methods, simulation techniques, and modeling tools, Probability and Statistics for Computer Scientists helps students solve problems and make optimal decisions in uncertain conditions
| Author | : J. Susan Milton |
| Publisher | : McGraw-Hill Europe |
| Total Pages | : 450 |
| Release | : 2012-11 |
| Genre | : Electronic data processing |
| ISBN | : 9780071087858 |
Helps students to understand statistical methods and reasoning as well as practice in using them. This book includes examples and exercises that are specially chosen for those looking for careers in the engineering and computing sciences. It is intended as a first course in probability and applied statistics for students.
| Author | : Janet Susan Milton |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 1986 |
| Genre | : Electronic data processing |
| ISBN | : 9780071005746 |
| Author | : Sheldon M. Ross |
| Publisher | : Elsevier |
| Total Pages | : 641 |
| Release | : 2004-07-21 |
| Genre | : Mathematics |
| ISBN | : 0080470319 |
Introduction to Probability and Statistics for Engineers and Scientists, Third Edition, provides an introduction to applied probability and statistics for engineering or science majors . This updated text emphasizes the manner in which probability yields insight into statistical problems, ultimately resulting in an intuitive understanding of the statistical procedures most often used by practicing engineers and scientists. The Third Edition includes new exercises, examples, homework problems, updated statistical material, and more. New exercises and data examples include: the one-sided Chebyshev inequality for data; logistics distribution and logistic regression; estimation and testing in proofreader problems; and product form estimates of life distributions. Real data sets are incorporated in a wide variety of exercises and examples throughout the book, and the enclosed CD-ROM includes unique, easy-to-use software that automates the required computations. This book is intended primarily for undergraduates in engineering and the sciences, and would be of particular interest to students in Industrial Engineering, Operations Research, Statistics, Mathematics, Computer Science, Electrical Engineering, Civil Engineering, Chemical Engineering, and Quantitative Business. It could also be of value in a graduate introductory course in probability and statistics. New in this edition: * New exercises and data examples including: - The One-sided Chebyshev Inequality for Data - The Logistics Distribution and Logistic Regression - Estimation and Testing in proofreader problems - Product Form Estimates of Life Distributions - Observational Studies * Updated statistical material * New, contemporary applications Hallmark features: * Reflects Sheldon Ross's masterfully clear exposition * Contains numerous examples, exercises, and homework problems * Unique, easy-to-use software automates required computations * Applies probability theory to everyday statistical problems and situations * Careful development of probability, modeling, and statistical procedures leads to intuitive understanding * Instructor's Solutions Manual is available to adopters
