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An essential guide to the concepts of probability theory that puts the focus on models and applications
This book offers an authoritative text that presents the main ideas and concepts, as well as the theoretical background, models, and applications of probability. The authors—noted experts in the field—include a review of problems where probabilistic models naturally arise, discuss the appropriate statistical methods, and explain how these models fit into the data presented. To aid in understanding, the book presents many real-world exercises and solutions that appear after each section within a chapter. A wide-range of topics are covered that include concepts of probability, univariate discrete distribution, univariate continuous distributions, bivariate discrete random variables, bivariate continuous random variables, stochastic independence-multivariate random variables, and many more. Designed as a useful guide, the text contains theory of probability, definitions, charts, examples, illustrations, problems and solutions, and a glossary.
This important text:
Includes classroom-tested problems and solutions to probability exercises
Highlights real-world exercises designed to make clear the concepts presented
Uses MatLAB software to illustrate the text’s computer exercises
Features applications representing worldwide situations and processes
Offers a Student Solutions Manual that contains select solutions to numerous exercises found in the book
Written for students majoring in statistics, engineering, operations research, computer science, physics, and mathematics, Introduction to Probability: Models and Applications is an accessible text that explores the basic concepts of probability and includes detailed information on models and applications