312 Book Reviews
Probability and Statistics for Computer Scientists M.Baron, 2006
Boca Raton, Chapman and Hall–CRC 414 pp., $89.95
ISBN 978-1-584-88641-9
An improvement over an earlier similar title by Johnson (2003),Probability and Statistics for Com-puter Scienceby Michael Baron, Professor of Sta-tistics at the University of Texas at Dallas, is a modern text with features that make it an ideal textbook for computer science students.
Emphasizing methodology and applications, the book begins with the fundamental rules of proba-bility and distributions, followed by stochastic pro-cesses, Monte Carlo methods, Markov chains and queuing theory tools, such as discrete and contin-uous time queuing systems.
The final chapters cover topics in statistics, esti-mation, testing, regression and model fitting. The appendix reviews calculus and linear algebra, including basic methods of differentiation, inte-gration, matrix manipulation, evaluation of limits and series.
Assuming one or two semesters of college calcu-lus, the book is illustrated throughout with numer-ous examples, exercises, figures and tables that stress direct applications in computer science and software engineering. It also provides MATLAB code and demonstrations that are written in simple commands that can be directly translated into other computer languages.
This book is primarily intended for junior un-dergraduate to beginning graduate level students majoring in computer-related fields. It can also be used by electrical engineering, mathematics, stat-istics, actuarial science and other majors for a standard introductory statistics course.
Graduate students can use this book to prepare for probability-based courses such as queuing theory, artificial neural networks and computer performance.
Overall, this well-written text can be used as a standard reference on probability and statistical methods, simulation and modelling tools.
By the end of this course, advanced undergradu-ate and beginning graduundergradu-ate students should be able to read a word problem or a corporate report, real-ize the uncertainty that is involved in the situation described, select a suitable probability model, esti-mate and test its parameters on the basis of real data, compute probabilities of interesting events and other vital characteristics, and make appro-priate conclusions and forecasts.
Though this book satisfies the Accreditation Board for Engineering and Technology require-ments for probability and statistics, we may regret
that the chapter on regression analysis does not deal as thoroughly as we would expect from a modern text. Let us hope that this shortcoming will be overcome in the next edition of the book.
Reference
Johnson, J. L. (2003) Probability and Statistics for Computer Science. New York: Wiley.
Naguib Lallmahomed Mauritius
Latent Curve Models: a Structural Equation Perspective
K. A. Bollen andP. J. Curran, 2006 London, Wiley
286 pp., £50.30
ISBN 978-0-471-45592-9
Growth curve models remain one of the most com-monly used statistical methods for the analysis of longitudinal data. In this volume, Bollen and Cur-ran provide a compendium of variants of this family of techniques from a structural equation modelling (SEM) perspective. The book is entitled latent curve models because, in the SEM literature, the case-specific parameters controlling for the effect of time are treated as latent variables.
The first half of the theoretical development by Bollen and Curran concentrates on unconditional latent growth models, i.e. models where only the marginal distribution of a specific variable is con-sidered. Bollen and Curran have provided a very good treatment of identification issues in latent curve models in particular, and SEM in general. The book contains specific advice on the number of waves of measurements that are necessary to esti-mate each of the models that are reviewed. There is a particularly illuminating description of the iden-tification of a quadratic latent curve model in the appendix of Chapter 4.
This publication also describes how latent growth curve models can be adapted to non-linear func-tions of the time variable. A chapter also covers the analysis of groups, which is what is routinely conducted in practice. The last two chapters pro-vide some more general material on multivariate extensions of the latent growth models as well as a general framework to compute all the models that are presented in earlier chapters.
