Probability and Statistics for Computer Scientists (3rd Ed.)
Auteur : Baron Michael
Praise for the Second Edition:
"The author has done his homework on the statistical tools needed for the particular challenges computer scientists encounter... [He] has taken great care to select examples that are interesting and practical for computer scientists. ... The content is illustrated with numerous figures, and concludes with appendices and an index. The book is erudite and ? could work well as a required text for an advanced undergraduate or graduate course." ---Computing Reviews
Probability and Statistics for Computer Scientists, Third Edition helps students understand fundamental concepts of Probability and Statistics, general methods of stochastic modeling, simulation, queuing, and statistical data analysis; make optimal decisions under uncertainty; model and evaluate computer systems; and prepare for advanced probability-based courses. Written in a lively style with simple language and now including R as well as MATLAB, this classroom-tested book can be used for one- or two-semester courses.
Features:
- Axiomatic introduction of probability
- Expanded coverage of statistical inference and data analysis, including estimation and testing, Bayesian approach, multivariate regression, chi-square tests for independence and goodness of fit, nonparametric statistics, and bootstrap
- Numerous motivating examples and exercises including computer projects
- Fully annotated R codes in parallel to MATLAB
- Applications in computer science, software engineering, telecommunications, and related areas
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).
About the Author
Michael Baron
1. Introduction and Overview 2. Probability 3. Discrete Random Variables and Their Distributions 4. Continuous Distributions5. Computer Simulations and Monte Carlo Methods6. Stochastic Processes 7. Queuing Systems 8. Introduction to Statistics9. Statistical Inference I10. Statistical Inference II
11. Regression12. Appendix
Michael Baron is a professor of statistics at the American University in Washington, DC. He has published two books and numerous research articles and book chapters. Dr. Baron is a fellow of the American Statistical Association, a member of the International Society for Bayesian Analysis, and an associate editor of the Journal of Sequential Analysis. In 2007, he was awarded the Abraham Wald Prize in Sequential Analysis. His research focuses on the use of sequential analysis, change-point detection, and Bayesian inference in epidemiology, clinical trials, cyber security, energy, finance, and semiconductor manufacturing. He received a Ph.D. in statistics from the University of Maryland.
Date de parution : 07-2019
17.8x25.4 cm
Thèmes de Probability and Statistics for Computer Scientists :
Mots-clés :
Antivirus Software; Cumulative Distribution Function; statistics textbooks; Independent Exponential Times; computational statistics; Negative Binomial; MATLAB; Transition Probability Matrix; R software; Steady State Distribution; mathematics; MATLAB's Statistics Toolbox; Markov chain; Conditional Expectation; Monte Carlo method; Interarrival Time; normal distribution; Regular Markov Chain; parametric statistics; Independent Bernoulli Trials; Poisson distribution; Random Variable; probability; Queuing System; probability axioms; Intercept Intercept; Prediction Interval; sets; Scatter Plots; simulation; standard deviation; Expected Response Time; statistical inference; Monte Carlo Study; statistics; Regression Model; stochastic process; Cpu Time; variance; Inverse Transform Method; median; Queuing Process; linear regression; Dummy Variables; likelihood; Poisson Process; fitting; estimator; descriptive statistics; data analysis; correlation; conditional probability; computer science; combinatorics; binomial distribution; approximation; ANOVA; analysis of variance