Advanced mathematics for engineers with applications in stochastic processes
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Contributions
- Lian, Jian-ao - Contributor
- Mishev, D. P. (Dimiter P.) - Contributor
Publication
2010 - Nova Science Publishers, New York, New York (State)
Language
English
Word Count
137,500 words, Guess
Page Count
550 pages
Identifiers
- ISBN-101608768805
- ISBN-139781608768806
- Library of Congress Control Number2009044328
- OCLC Control Number462923313
- Open LibraryOL24408849M
Classifications
- DDC510
- LCCQA331 .H175 2010
Description
Topics in advanced mathematics for engineers, probability and statistics typically span three subject areas, are addressed in three separate textbooks and taught in three different courses in as many as three semesters. Due to this arrangement, students taking these courses have had to shelf some important and fundamental engineering courses until much later than is necessary. This practice has generally ignored some striking relations that exist between the seemingly separate areas of statistical concepts, such as moments and estimation of Poisson distribution parameters. On one hand, these concepts commonly appear in stochastic processes - for instance, in measures on effectiveness in queuing models. On the other hand, they can also be viewed as applied probability in engineering disciplines - mechanical, chemical, and electrical, as well as in engineering technology. There is obviously, an urgent need for a textbook that recognizes the corresponding relationships between the various areas and a matching cohesive course that will see through to their fundamental engineering courses as early as possible. This book is designed to achieve just that. Its seven chapters, while retaining their individual integrity, flow from selected topics in advanced mathematics such as complex analysis and wavelets to probability, statistics and stochastic processes.
Subjects
Series Statement
- Mathematics research developments
- Mathematics research developments series
Other Editions
- Advanced mathematics for engineers with applications in stochastic processes
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