Probability and Statistics with Reliability, Queuing, and Computer Science Applications


Book Description

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.




Computer Algebra in Scientific Computing


Book Description

Proceedings of the Third Workshop on Computer Algebra in Scientific Computing, Samarkand, Octobe5r 5-9, 2000




The Mathematica Journal


Book Description




Efficient Go


Book Description

With technological advancements, fast markets, and higher complexity of systems, software engineers tend to skip the uncomfortable topic of software efficiency. However, tactical, observability-driven performance optimizations are vital for every product to save money and ensure business success. With this book, any engineer can learn how to approach software efficiency effectively, professionally, and without stress. Author Bartłomiej Płotka provides the tools and knowledge required to make your systems faster and less resource-hungry. Efficient Go guides you in achieving better day-to-day efficiency using Go. In addition, most content is language-agnostic, allowing you to bring small but effective habits to your programming or product management cycles. This book shows you how to: Clarify and negotiate efficiency goals Optimize efficiency on various levels Use common resources like CPU and memory effectively Assess efficiency using observability signals like metrics, logging, tracing, and (continuous) profiling via open source projects like Prometheus, Jaeger, and Parca Apply tools like go test, pprof, benchstat, and k6 to create reliable micro and macro benchmarks Efficiently use Go and its features like slices, generics, goroutines, allocation semantics, garbage collection, and more!




CMG '92, Reno


Book Description




Java Performance Tuning


Book Description

Java application performance is tied pretty heavily to the underlying Java Virtual Machine, and the new 1.4 version of Java has significant changes that mean previously used performance tips and strategies may no longer work. Significantly revised and expanded, this second edition not only covers Java 1.4, but adds new coverage of JDBC, NIO, Servlets, EJB and JavaServer Pages. Suitable for intermediate and advanced Java developers, this text also covers JDBC, RMI/CORBA, Servlets, JavaServer Pages and custom tag libraries, XML, internationalization, JavaMail, Enterprise JavaBeans and performance tuning. It should be a useful resource for teaching how to create a tuning strategy, how to use profiling tools to understand a program's behaviour, and how to avoid performance penalties from inefficient code, making them more efficient and effective. The result is code that's robust, maintainable and fast.




An Introduction to Modern Mathematical Computing


Book Description

Thirty years ago mathematical, as opposed to applied numerical, computation was difficult to perform and so relatively little used. Three threads changed that: the emergence of the personal computer; the discovery of fiber-optics and the consequent development of the modern internet; and the building of the Three “M’s” Maple, Mathematica and Matlab. We intend to persuade that Mathematica and other similar tools are worth knowing, assuming only that one wishes to be a mathematician, a mathematics educator, a computer scientist, an engineer or scientist, or anyone else who wishes/needs to use mathematics better. We also hope to explain how to become an "experimental mathematician" while learning to be better at proving things. To accomplish this our material is divided into three main chapters followed by a postscript. These cover elementary number theory, calculus of one and several variables, introductory linear algebra, and visualization and interactive geometric computation.




An Introduction to Programming with Mathematica®


Book Description

Accompanying the book, as with all TELOS sponsored publications, is an electronic component. In this case it is a DOS-Diskette produced by one of the coauthors, Paul Wellin. This diskette consists of Mathematica notebooks and packages which contain the codes for all examples and exercises in the book, as well as additional materials intended to extend many ideas covered in the text. It is of great value to teachers, students, and others using this book to learn how to effectively program with Mathematica .




Advanced Machine Learning Technologies and Applications


Book Description

This book constitutes the refereed proceedings of the First International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2012, held in Cairo, Egypt, in December 2012. The 58 full papers presented were carefully reviewed and selected from 99 intial submissions. The papers are organized in topical sections on rough sets and applications, machine learning in pattern recognition and image processing, machine learning in multimedia computing, bioinformatics and cheminformatics, data classification and clustering, cloud computing and recommender systems.