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Books > Science & Mathematics > Mathematics > Probability & statistics
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Probability and Statistics for Computer Scientists (Hardcover, 3rd edition)
Loot Price: R3,548
Discovery Miles 35 480
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Probability and Statistics for Computer Scientists (Hardcover, 3rd edition)
Expected to ship within 9 - 17 working days
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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.
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