Probability and Statistics for Computer Science

ISBN-13: 9780471326724 / Angielski / Twarda / 2003 / 760 str.

James L. Johnson

Opis

Comprehensive and thorough development of both probability and statistics for serious computer scientists; goal-oriented: "to present the mathematical analysis underlying probability results"
Special emphases on simulation and discrete decision theory
Mathematically-rich, but self-contained text, at a gentle pace
Review of calculus and linear algebra in an appendix
Mathematical interludes (in each chapter) which examine mathematical techniques in the context of probabilistic or statistical importance
Numerous section exercises, summaries, historical notes, and Further Readings for reinforcement of content

Recenzje

"This text will fill a gap in the education of a sophisticated computer science student who has a firm base in mathematics and statistics." ( Computing Reviews, May 7, 2009)

" this textbook would be ideal." (The American Statistician, February 2006)

"This is really a statistics textbook written explicitly for undergraduate computer science majors I found the numerous examples of the use of statistics within the field of computer science extremely informative." (Technometrics, November 2004)

"Thorough, in–depth, relatively complete and rigorous introduction to the statistics a CS professional should know." (American Mathematical Monthly, August 2004)

"This is a rigorous introductory text in probability and statistics, which also develops in a rigorous fashion all the necessary supporting mathematics beyond calculus and algebra." (Mathematical Reviews, issue 2004i)

"...one–of–a–kind resource...proves an ideal resource for computer science students and practitioners interested in a probability study..." (Zentralblatt Math, Vol. 1027, 2004)

...presents introductory topics in probability and statistics with particular emphasis on concepts that arise in computer science...disguised also by the feature that it develops all necessary supporting mathematics in a thorough and rigorous fashion. (Quarterly of Applied Mathematics, Vol. LXI, No. 4, December 2003)

Spis treści

Preface.

1. Combinatorics and Probability.

2. Discrete Distributions.

3. Simulation.

4. Discrete Decision Theory.

5. Real Line–Probability.

6. Continuous Distributions.

7. Parameter Estimation.

Appendix A. Analytical Tools.

Appendix B. Statistical Tables.

Bibliography.

Index.

O autorze

JAMES L. JOHNSON holds a PhD in Mathematics and has twenty–five years experience in academic and industrial computer science. He is currently Professor of Computer Science at Western Washington University. He is also the author of Database: Models, Languages, Design.

Blurb

A unique probability study for computer science students

While many computer science curricula include only an introductory course on general probability, there is a recognized need for further study of this mathematical discipline within the specific context of computer science. Probability and Statistics for Computer Science develops introductory topics in probability with this particular emphasis, providing computer science students with an invaluable resource in their continued st udies and professional research.

James Johnson s text begins with the basic definitions of probability distributions and random variables and then elaborates their properties and applications. Probability and Statistics for Computer Science treats the most common discrete and continuous distributions, showing how they find use in decision and estimation problems, and constructs computer algorithms for generating observations from the various distributions. This one–of–a–kind resource also:

• Includes a thorough and rigorous development of all the necessary supporting mathematics
• Provides an opportunity to reconnect applications with the theoretical concepts of distributions introduced in prerequisite courses
• Gathers supporting topics in an appendix: set theory, limit processes, real number structure, Riemann–Stieltjes integrals, matrix transformation, and determinants
• Uses computer science examples from computer science such as client–server performance evaluation and image processing

The author also addresses a variety of supporting topics, such as estimation arguments with limits, properties of power series, and Markov processes. Johnson s text proves an ideal resource for computer science students and practitioners interested in a probability study specific to their field.