Advanced Mathematics for Engineers with Applications in Stochastic Processes

Advanced Mathematics for Engineers with Applications in Stochastic Processes
Author :
Publisher : Nova Science Publishers
Total Pages : 0
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ISBN-10 : 1608768805
ISBN-13 : 9781608768806
Rating : 4/5 (05 Downloads)

Book Synopsis Advanced Mathematics for Engineers with Applications in Stochastic Processes by : Aliakbar Montazer Haghighi

Download or read book Advanced Mathematics for Engineers with Applications in Stochastic Processes written by Aliakbar Montazer Haghighi and published by Nova Science Publishers. This book was released on 2010 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 recognises 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.


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