Applied Probability
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Beschreibung
Probability (including stochastic processes) is now being applied to virtually every academic discipline, especially to the sciences. An area of substantial application is that known as operations research or industrial engineering, which incorporates subjects such as queueing theory, optimization, and network flow. This book provides a compact introduction to that field for students with minimal preparation, knowing mainly calculus and having "mathe matical maturity." Beginning with the basics of probability, the develop ment is self-contained but not abstract, that is, without measure theory and its probabilistic counterpart. Although the text is reasonably short, a course based on this book will normally occupy two semesters or three quarters. There are many points in the discussions and problems which require the assistance of an instructor for completeness and clarity. The book is designed to give equal emphasis to those applications which motivate the subject and to appropriatemathematical techniques. Thus, the student who has successfully completed the course is ready to turn in either of two directions: towards direct study of research papers in operations research, or towards a course in abstract probability, for which this text provides the intuitive background. Frank A. Haight Pennsylvania State University vii Contents 1. Discrete Probability .................................................. 1 1.1. Applied Probability. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2. Sample Spaces ......................................................... 3 1.3. Probability Distributions and Parameters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.4. The Connection between Distributions and Sample Points: Random Variables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 . . . . . . . . . . . . . . . . . . .
Autor*in
Frank A. Haight
Themen in »Applied Probability«
Markov chain Random variable Variance binomial distribution conditional probability measure theory operations research optimization probability probability distribution stochastic process