Probability Models
By: by John Haigh
Contributors: SpringerLink (Online service)
Edition: 2nd ed. 2013.
Series: Springer Undergraduate Mathematics Series
Published: 2013
Language: eng
Format: [electronic resource] /
Number of pages: 287
ISBN-13: 9781447153436
Subjects: Probability Theory and Stochastic Processes; Operation Research/Decision Theory; Mathematical Applications in the Physical Sciences; Mathematical Applications in Computer Science; Probability and Statistics in Computer Science; Distribution (Probability theory); Simulation and Modeling; Computer simulation; Computer science; Mathematics; Operations research
Description: The purpose of this book is to provide a sound introduction to the study of real-world phenomena that possess random variation. It describes how to set up and analyse models of real-life phenomena that involve elements of chance. Motivation comes from everyday experiences of probability, such as that of a dice or cards, the idea of fairness in games of chance, and the random ways in which, say, birthdays are shared or particular events arise. Applications include branching processes, random walks, Markov chains, queues, renewal theory, and Brownian motion. This popular second edition textbook contains many worked examples and several chapters have been updated and expanded. Some mathematical knowledge is assumed. The reader should have the ability to work with unions, intersections and complements of sets; a good facility with calculus, including integration, sequences and series; and appreciation of the logical development of an argument. Probability Models is designed to aid students studying probability as part of an undergraduate course on mathematics or mathematics and statistics.
Table of contents: title: Probability Spaces; title: Conditional Probability and Independence; title: Common Probability Distributions; title: Random Variables; title: Sums of Random Variables; title: Convergence and Limit Theorems; title: Stochastic Processes in Discrete Time; title: Stochastic Processes in Continuous Time; title: Appendix: Common Distributions and Mathematical Facts.