Probability And Statistics For Computer Scientists - Baron Michael

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Résumé :
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(R) 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.

Biographie:
Michael Baron is a professor of statistics at the University of Texas at Dallas. 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.

Sommaire:
Introduction and Overview Making decisions under uncertainty Overview of this book Probability and Random Variables Probability Sample space, events, and probability Rules of Probability Equally likely outcomes. Combinatorics Conditional probability. Independence Discrete Random Variables and Their Distributions Distribution of a random variable Distribution of a random vector Expectation and variance Families of discrete distributions Continuous Distributions Probability density Families of continuous distributions Central limit theorem Computer Simulations and Monte Carlo Methods Introduction Simulation of random variables Solving problems by Monte Carlo methods Stochastic Processes Stochastic Processes Definitions and classifications Markov processes and Markov chains Counting processes Simulation of stochastic processes Queuing Systems Main components of a queuing system The Little's Law Bernoulli single-server queuing process M/M/1 system Multiserver queuing systems Simulation of queuing systems Statistics Introduction to Statistics Population and sample, parameters and statistics Simple descriptive statistics Graphical statistics Statistical Inference I Parameter estimation Confidence intervals Unknown standard deviation Hypothesis testing Inference about variances Statistical Inference II Chi-square tests Nonparametric statistics Bootstrap Bayesian inference Regression Least squares estimation Analysis of variance, prediction, and further inference Multivariate regression Model building Appendix Appendix Inventory of distributions Distribution tables Calculus review Matrices and linear systems Answers to selected exercises Index Summary, Conclusions, and Exercises are included at the end of each chapter.