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Baron M. Probability and Statistics for Computer Scientists

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Baron M. Probability and Statistics for Computer Scientists
2nd ed. — CRC Press, 2014. — 469 p. — ISBN: 9781482214109.
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 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.
Introduction and Overview.
Making decisions under uncertainty.
Overview of this book.
Probability and Random Variables.
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.
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.
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.
Bayesian inference.
Least squares estimation.
Analysis of variance, prediction, and further inference.
Multivariate regression.
Model building.
Inventory of distributions.
Distribution tables.
Calculus review.
Matrices and linear systems.
Answers to selected exercises.
Summary, Conclusions, and Exercises are included at the end of each chapter.
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