Advanced Mathematics for Engineers With Applications in Stochastic Processes (Mathematics Research Developments)

Aliakbar Montazer Haghighi, Jian-ao Lian, Dimitar P. Mishev

ISBN 10: 1608768805 / ISBN 13: 9781608768806

Published by Nova Science Publishers, Inc. (edition UK ed.), 2010

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Seller Inventory # 1608768805-9-1

Bibliographic Details

• Title: Advanced Mathematics for Engineers With Applications in Stochastic Processes (Mathematics Research Developments)
• Publisher: Nova Science Publishers, Inc. (edition UK ed.)
• Publication Date: 2010
• Language: English
• ISBN 10: 1608768805
• ISBN 13: 9781608768806
• Binding: Hardcover
• Edition: UK ed.
• Condition: New

About this title

Synopsis

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.