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Author: Jean-François Mari
Publisher: Springer Science & Business Media
Published: 2013-04-17
Total Pages: 243
ISBN-13: 1475762801
DOWNLOAD EBOOK →Probabilistic and Statistical Methods in Computer Science
Author: Katarzyna Stapor
Publisher: Springer
Published: 2021-05-23
Total Pages: 157
ISBN-13: 9783030458010
DOWNLOAD EBOOK →This book strikes a healthy balance between theory and applications, ensuring that it doesn’t offer a set of tools with no mathematical roots. It is intended as a comprehensive and largely self-contained introduction to probability and statistics for university students from various faculties, with accompanying implementations of some rudimentary statistical techniques in the language R. The content is divided into three basic parts: the first includes elements of probability theory, the second introduces readers to the basics of descriptive and inferential statistics (estimation, hypothesis testing), and the third presents the elements of correlation and linear regression analysis. Thanks to examples showing how to approach real-world problems using statistics, readers will acquire stronger analytical thinking skills, which are essential for analysts and data scientists alike.
Author: David Forsyth
Publisher: Springer
Published: 2017-12-13
Total Pages: 367
ISBN-13: 3319644106
DOWNLOAD EBOOK →This textbook is aimed at computer science undergraduates late in sophomore or early in junior year, supplying a comprehensive background in qualitative and quantitative data analysis, probability, random variables, and statistical methods, including machine learning. With careful treatment of topics that fill the curricular needs for the course, Probability and Statistics for Computer Science features: • A treatment of random variables and expectations dealing primarily with the discrete case. • A practical treatment of simulation, showing how many interesting probabilities and expectations can be extracted, with particular emphasis on Markov chains. • A clear but crisp account of simple point inference strategies (maximum likelihood; Bayesian inference) in simple contexts. This is extended to cover some confidence intervals, samples and populations for random sampling with replacement, and the simplest hypothesis testing. • A chapter dealing with classification, explaining why it’s useful; how to train SVM classifiers with stochastic gradient descent; and how to use implementations of more advanced methods such as random forests and nearest neighbors. • A chapter dealing with regression, explaining how to set up, use and understand linear regression and nearest neighbors regression in practical problems. • A chapter dealing with principal components analysis, developing intuition carefully, and including numerous practical examples. There is a brief description of multivariate scaling via principal coordinate analysis. • A chapter dealing with clustering via agglomerative methods and k-means, showing how to build vector quantized features for complex signals. Illustrated throughout, each main chapter includes many worked examples and other pedagogical elements such as boxed Procedures, Definitions, Useful Facts, and Remember This (short tips). Problems and Programming Exercises are at the end of each chapter, with a summary of what the reader should know. Instructor resources include a full set of model solutions for all problems, and an Instructor's Manual with accompanying presentation slides.
Author: James L. Johnson
Publisher: John Wiley & Sons
Published: 2011-09-09
Total Pages: 764
ISBN-13: 1118165969
DOWNLOAD EBOOK →Comprehensive and thorough development of both probability and statistics for serious computer scientists; goal-oriented: "to present the mathematical analysis underlying probability results" Special emphases on simulation and discrete decision theory Mathematically-rich, but self-contained text, at a gentle pace Review of calculus and linear algebra in an appendix Mathematical interludes (in each chapter) which examine mathematical techniques in the context of probabilistic or statistical importance Numerous section exercises, summaries, historical notes, and Further Readings for reinforcement of content
Author: Michael Baron
Publisher: CRC Press
Published: 2013-08-05
Total Pages: 475
ISBN-13: 1439875901
DOWNLOAD EBOOK →Student-Friendly Coverage of Probability, Statistical Methods, Simulation, and Modeling Tools Incorporating feedback from instructors and researchers who used the previous edition, Probability and Statistics for Computer Scientists, Second Edition helps students understand general methods of stochastic modeling, simulation, and data analysis; make optimal decisions under uncertainty; model and evaluate computer systems and networks; and prepare for advanced probability-based courses. Written in a lively style with simple language, this classroom-tested book can now be used in both one- and two-semester courses. New to the Second Edition Axiomatic introduction of probability Expanded coverage of statistical inference, including standard errors of estimates and their estimation, inference about variances, chi-square tests for independence and goodness of fit, nonparametric statistics, and bootstrap More exercises at the end of each chapter Additional MATLAB® codes, particularly new commands of the Statistics Toolbox In-Depth yet Accessible Treatment of Computer Science-Related Topics Starting with the fundamentals of probability, the text takes students through topics heavily featured in modern computer science, computer engineering, software engineering, and associated fields, such as computer simulations, Monte Carlo methods, stochastic processes, Markov chains, queuing theory, statistical inference, and regression. It also meets the requirements of the Accreditation Board for Engineering and Technology (ABET). Encourages Practical Implementation of Skills Using simple MATLAB commands (easily translatable to other computer languages), the book provides short programs for implementing the methods of probability and statistics as well as for visualizing randomness, the behavior of random variables and stochastic processes, convergence results, and Monte Carlo simulations. Preliminary knowledge of MATLAB is not required. Along with numerous computer science applications and worked examples, the text presents interesting facts and paradoxical statements. Each chapter concludes with a short summary and many exercises.
Author: Daniel Neuenschwander
Publisher: Springer Science & Business Media
Published: 2004-04-30
Total Pages: 155
ISBN-13: 3540220011
DOWNLOAD EBOOK →Cryptology nowadays is one of the most important areas of applied mathematics, building on deep results and methods from various areas of mathematics. This text is devoted to the study of stochastic aspects of cryptology. Besides classical topics from cryptology, the author presents chapters on probabilistic prime number tests, factorization with quantum computers, random-number generators, pseudo-random-number generators, information theory, and the birthday paradox and meet-in-the-middle attack. In the light of the vast literature on stochastic results relevant for cryptology, this book is intended as an invitation and introduction for students, researchers, and practitioners to probabilistic and statistical issues in cryptology.
Author: Kishor S. Trivedi
Publisher: John Wiley & Sons
Published: 2016-06-30
Total Pages: 880
ISBN-13: 1119314208
DOWNLOAD EBOOK →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: Michael Baron
Publisher: CRC Press
Published: 2018-11-14
Total Pages: 427
ISBN-13: 1420011421
DOWNLOAD EBOOK →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: Norm Matloff
Publisher: Orange Grove Text Plus
Published: 2009-09
Total Pages: 0
ISBN-13: 9781616100360
DOWNLOAD EBOOK →Author: Gilles Barthe
Publisher: Cambridge University Press
Published: 2020-12-03
Total Pages: 583
ISBN-13: 110848851X
DOWNLOAD EBOOK →This book provides an overview of the theoretical underpinnings of modern probabilistic programming and presents applications in e.g., machine learning, security, and approximate computing. Comprehensive survey chapters make the material accessible to graduate students and non-experts. This title is also available as Open Access on Cambridge Core.
