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230516 ||| eng |
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|a 1394165412
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|a 1394165420
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|a 9781394165421
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|a 1394165404
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|a 9781394165407
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4 |
|a QA274.2
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| 100 |
1 |
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|a Chakravarthy, Srinivas R.
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| 245 |
0 |
0 |
|a Introduction to matrix-analytic methods in queues, 1: Analytical and simulation approach - basics
|c Srinivas R. Chakravarthy
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| 246 |
3 |
1 |
|a Analytical and simulation approach - basics
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| 260 |
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|a London, UK
|b ISTE Ltd.
|c 2022
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| 300 |
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|a 1 online resource
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| 505 |
0 |
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|a Includes bibliographical references and index
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| 653 |
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|a Méthodes d'analyse matricielle
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| 653 |
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|a Théorie des files d'attente
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| 653 |
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|a Matrix analytic methods / http://id.loc.gov/authorities/subjects/sh2006001830
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| 653 |
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|a Matrix analytic methods / fast
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| 653 |
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|a Queuing theory / http://id.loc.gov/authorities/subjects/sh85109832
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| 653 |
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|a Queuing theory / fast
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 490 |
0 |
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|a Mathematics and statistics series
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| 015 |
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|a GBC2F5708
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| 776 |
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|z 1394165404
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| 776 |
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|z 9781394165421
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| 776 |
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|z 1786307324
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| 776 |
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|z 9781786307323
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| 776 |
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|z 1394165420
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| 776 |
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|z 9781394165407
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| 776 |
|
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|z 9781394165414
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| 856 |
4 |
0 |
|u https://learning.oreilly.com/library/view/~/9781786307323/?ar
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 519.22
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| 520 |
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|a Matrix-analytic methods (MAM) were introduced by Professor Marcel Neuts and have been applied to a variety of stochastic models since. In order to provide a clear and deep understanding of MAM while showing their power, this book presents MAM concepts and explains the results using a number of worked-out examples. This book's approach will inform and kindle the interest of researchers attracted to this fertile field. To allow readers to practice and gain experience in the algorithmic and computational procedures of MAM, Introduction to Matrix Analytic Methods in Queues 1 provides a number of computational exercises. It also incorporates simulation as another tool for studying complex stochastic models, especially when the state space of the underlying stochastic models under analytic study grows exponentially. The book's detailed approach will make it more accessible for readers interested in learning about MAM in stochastic models
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