The role of probability in computer science has been growing for years and, in lieu of a tailored textbook, many courses have employed a variety of similar, but not entirely applicable, alternatives. To meet the needs of the computer science graduate student (and the advanced undergraduate), best-selling author Sheldon Ross has developed the premier probability text for aspiring computer scientists involved in computer simulation and modeling. The math is precise and easily understood. As with his other texts, Sheldon Ross presents very clear explanations of concepts and covers those probability models that are most in demand by, and applicable to, computer science and related majors and practitioners.
Sprache
Verlagsort
Verlagsgruppe
Elsevier Science Publishing Co Inc
Zielgruppe
Für höhere Schule und Studium
Primary market is emerging upper undergraduate courses devoted to probability (and statistics) for computer scientists. A strong secondary market will be to practitioners in the field
Maße
Höhe: 229 mm
Breite: 152 mm
Gewicht
ISBN-13
978-0-12-598051-7 (9780125980517)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Klassifikation
Dr. Sheldon M. Ross is a professor in the Department of Industrial and Systems Engineering at the University of Southern California. He received his PhD in statistics at Stanford University in 1968. He has published many technical articles and textbooks in the areas of statistics and applied probability. Among his texts are A First Course in Probability, Introduction to Probability Models, Stochastic Processes, and Introductory Statistics. Professor Ross is the founding and continuing editor of the journal Probability in the Engineering and Informational Sciences. He is a Fellow of the Institute of Mathematical Statistics, a Fellow of INFORMS, and a recipient of the Humboldt US Senior Scientist Award.
Autor*in
Professor, Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, USA
Review of Probability; Some Examples; Poisson and Compound Poisson Variables; Approximations and Processes; Markov Chains; Queuing; Random Algorithms and the Probabilistic Method; Martingales; Simulation.
