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Titlebook: Matrix-Analytic Methods in Stochastic Models; Guy Latouche,Vaidyanathan Ramaswami,David Yao Conference proceedings 2013 Springer Science+B

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發(fā)表于 2025-3-21 19:52:19 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Matrix-Analytic Methods in Stochastic Models
編輯Guy Latouche,Vaidyanathan Ramaswami,David Yao
視頻videohttp://file.papertrans.cn/628/627778/627778.mp4
概述The only forum on the theoretical, algorithmic and methodological aspects of matrix-analytic and related methods in stochastic models, and their application across various fields.This area of mathemat
叢書名稱Springer Proceedings in Mathematics & Statistics
圖書封面Titlebook: Matrix-Analytic Methods in Stochastic Models;  Guy Latouche,Vaidyanathan Ramaswami,David Yao Conference proceedings 2013 Springer Science+B
描述Matrix-analytic and related methods have become recognized as an important and fundamental approach for the mathematical analysis of general classes of complex stochastic models. Research in the area of matrix-analytic and related methods seeks to discover underlying probabilistic structures intrinsic in such stochastic models, develop numerical algorithms for computing functionals (e.g., performance measures) of the underlying stochastic processes, and apply these probabilistic structures and/or computational algorithms within a wide variety of fields. This volume presents recent research results on: the theory, algorithms and methodologies concerning matrix-analytic and related methods in stochastic models; and the application of matrix-analytic and related methods in various fields, which includes but is not limited to computer science and engineering, communication networks and telephony, electrical and industrial engineering, operations research, management science, financial and risk analysis, and bio-statistics. These research studies provide deep insights and understanding of the stochastic models of interest from a mathematics and/or applications perspective, as well as id
出版日期Conference proceedings 2013
關鍵詞Brownian Motion; Matrix-Analytic Methods; Operations research; Queueing Networks; Stochastic Models
版次1
doihttps://doi.org/10.1007/978-1-4614-4909-6
isbn_softcover978-1-4899-9424-0
isbn_ebook978-1-4614-4909-6Series ISSN 2194-1009 Series E-ISSN 2194-1017
issn_series 2194-1009
copyrightSpringer Science+Business Media New York 2013
The information of publication is updating

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沙發(fā)
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Springer Proceedings in Mathematics & Statisticshttp://image.papertrans.cn/m/image/627778.jpg
地板
發(fā)表于 2025-3-22 05:56:07 | 只看該作者
Majorization and Extremal PH Distributions, of phase-type distributions are found. Exponential distributions and Coxian distributions are identified to be extremal . distributions with respect to all the moments and Laplace–Stieltjes transforms for certain subsets of . distributions.
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發(fā)表于 2025-3-22 10:12:31 | 只看該作者
A Fluid Introduction to Brownian Motion and Stochastic Integration,al examples are presented setting the stage for the development of algorithms for stochastic integration via the well-studied and easily understood fluid flow models driven by finite state Markov chains.
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Matrix-Analytic Methods in Stochastic Models978-1-4614-4909-6Series ISSN 2194-1009 Series E-ISSN 2194-1017
8#
發(fā)表于 2025-3-22 21:17:36 | 只看該作者
Sophie Hautphenne,Guy Latouche,Giang T. Nguyen-Lehrbuch zur Numerik.Includes supplementary material: Numerische Mathematik ist ein zentrales Gebiet der Mathematik, das für vielf?ltige Anwendungen die Grundlage bildet und das alle Studierenden der Mathematik, Ingenieurwissenschaften, Informatik und Physik kennenlernen..Das vorliegende Lehrbuch i
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發(fā)表于 2025-3-23 04:52:52 | 只看該作者
Qi-Ming He,Hanqin Zhang,Juan C. Vera-Lehrbuch zur Numerik.Includes supplementary material: Numerische Mathematik ist ein zentrales Gebiet der Mathematik, das für vielf?ltige Anwendungen die Grundlage bildet und das alle Studierenden der Mathematik, Ingenieurwissenschaften, Informatik und Physik kennenlernen..Das vorliegende Lehrbuch i
10#
發(fā)表于 2025-3-23 09:05:52 | 只看該作者
A Compressed Cyclic Reduction for QBD processes with Low-Rank Upper and Lower Transitions, to reduce the computational cost of the cyclic reduction iteration. The proposed algorithm saves computation by performing multiplications and inversions of matrices of small size (equal to the rank instead of to the phase space dimension) and inherits the stability property of the customary cyclic
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