Palpable 发表于 2025-3-28 17:10:48

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打击 发表于 2025-3-28 19:17:55

The Product form Tool for Queueing Networks,lled product form. This computationally attractive form will be shown to be directly related to the principle of balance per station. This principle will be used to provide practical insights concerning the following questions

范例 发表于 2025-3-29 01:38:52

Book 2000systems, stochastic Petri-nets andsystems dealing with reliability - has benefited significantlyfrom these advances. The objective of this book is to make thesetopics accessible to researchers, graduate students, andpractitioners. Great care was taken to make the exposition as clear aspossible. Ever

Immobilize 发表于 2025-3-29 04:19:51

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PRISE 发表于 2025-3-29 09:49:27

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organism 发表于 2025-3-29 13:24:35

Techniques for System Dependability Evaluation,feration of computer and computer-based communication systems has contributed to this in no small measure. This chapter is thus a step in the direction of summarizing the techniques, tools and recent developments in the field of system dependability evaluation.

河潭 发表于 2025-3-29 18:05:21

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Prophylaxis 发表于 2025-3-29 20:00:07

Use of Characteristic Roots for Solving Infinite State Markov Chains,ur focus in this chapter is on the discrete-time case, we will show how the continuous-time case can be handled by the same techniques. The M/G/1 and G/M/1 classes can be solved using the matrix analytic method , and we will also discuss the relationship between the approac

muscle-fibers 发表于 2025-3-30 01:35:10

An Introduction to Numerical Transform Inversion and Its Application to Probability Models,by Gaver . (For more on the history of numerical transform inversion, see our earlier survey .) Hence, in the application of probability models to engineering, transforms became regarded more as mathematical toys than practical tools. Indeed, the conventional wis

cipher 发表于 2025-3-30 04:41:38

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查看完整版本: Titlebook: Computational Probability; Winfried K. Grassmann Book 2000 Springer Science+Business Media New York 2000 Markov Chains.Markov chain.Markov