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
is David Carroll Professor of Mathematics and Statistics at American
University in Washington D. C. He conducts research in sequential
analysis and optimal stopping, change-point detection, Bayesian
inference, and applications of statistics in epidemiology, clinical
trials, semiconductor manufacturing, and other fields. M. Baron is a
Fellow of the American Statistical Association and a recipient of the
Abraham Wald Prize for the best paper in Sequential Analysis and the
Regents Outstanding Teaching Award. M. Baron holds a Ph.D. in statistics
from the University of Maryland. In his turn, he supervised twelve
doctoral students, mostly employed on academic and research positions.
