Probability and Statistics for Computer Scientists

by Michael Baron

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.

Genres: Computer Science, Mathematics, Textbooks

506 pages, Kindle Edition

First published October 1, 2006

About the author

I grew up in the New York area and I’ve lived there my entire life. I worked in retail and taught high school English before I got my first book contract. I have gotten several additional book contracts since then, which is fortunate because I didn’t have the patience to work in retail and, while I quite enjoyed teaching, my approach was a bit too unconventional for most school systems. One school administrator told me that, “there are more important things than being a dynamic teacher.” Since I couldn’t name any of those things (at least in the context of school), I figured I didn’t have a long-term future in the profession. Hence, I became a writer, where I believe people appreciate a certain level of dynamism.


Though I started with nonfiction, I have always loved fiction and I have always wanted to write it. I’ve always had a particular affection for love stories. In fact, the very first book-length thing I ever wrote, when I was thirteen, was a love story. Mind you, it was the kind of love story that a thirteen-year-old boy would write, but it was a love story nonetheless. I have a deep passion for writing about relationships – family relationships, working relationships, friendships, and, of course, romantic relationships – and I can only truly explore this by writing fiction. These novels have given me a way to voice the millions of things running through my head.


My wife and kids are the center of my life. My wife is the inspiration for all of my love stories and my children enthrall me, challenge me, and keep me moving. One of the primary reasons I wrote my first novel, When You Went Away was that I wanted to write about being a father. Aside from my family, I have a few other burning passions. I’m a pop culture junkie with an especially strong interest in music, I love fine food (and any restaurant shaped like a hot dog), and I read far too many sports blogs for my own good.


You might have noticed that I haven't published a photo of myself. This isn't because I'm involved in the Witness Protection program or because I have an innate fear of cameras. It's because Michael Baron is a pseudonym. I'm writing these novels “undercover” because they're not entirely compatible with the nonfiction books I write and I didn't want to confuse readers. We're all different people sometimes, right? I just decided to give my alter ego another name.


My third novel, The Journey Home, has just come out. It’s a love story, too. Naturally.

Reviews

[Emil Petersen · Rating 4 out of 5]

I have read chapters 1-9 thoroughly; 10-12 not so much. The core material is solid and well explained. Lots of examples and convenient definitions here and there. Also a ton of useful exercises. A minor comment is that the examples are not really CS problems if you ask me, but rather general problems using CS words. Like jobs to a printer, number of emails received and things like that. This should not denigrate the book, because it really is very, very useful. Much of the statistical magic going on suddenly makes more sense. I have no regrets reading this (and it took quite a while).

[mirela Darau · Rating 4 out of 5]

- found it suitable for my Computer Science students. The main reasons for choosing it were the average mathematical level (theory is presented in a fairly simple mathematical context, which was great for my students), a good list of examples/exercises (ranging from very simple to rather complex, comprising computer science problems as well as famous probability ones, and others), and the convenient structure.

However, I found some of its sections pretty dry (like Descriptive Statistics or Continuous Distributions) and I wished for a couple of more complex problems in each chapter, as I find it good to challenge to the extreme also the abilities of good students :)

[I used it together with J.L. Johnson's book - to provide some more hard-core stuff - and with A. Vicker's statistics book - to get some spicy, practical examples]

[Darin · Rating 5 out of 5]

This has become my favorite introductory statistics books. What makes this different from many other statistics books is that the author states things that most authors assume (incorrectly) will be obvious to the beginner. Standard introductory material is included as well as chapters on simulation and stochastic processes that will be helpful to some computer scientists. Many of the examples are based on computer science, which is a plus for computer science majors, but shouldn't be a problem for non-computer science majors. There are some examples implemented in Matlab, although not much.

[Tim · Rating 5 out of 5]

As a CS major, I found this book perfectly geared for the intersection of Computer Science and Probabilities/Statistics. It covers the major application areas of the latter in the former in a relevant and efficient manner. It was used as the text book for a graduate level course that I took, and even though the professor turned the class into an undergraduate level statistics class, the book itself has been an excellent reference for my PhD work.

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