Titlebook: Conditional Monte Carlo; Gradient Estimation Michael Fu,Jian-Qiang Hu Book 19971st edition Springer Science+Business Media New York 1997 I

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书目名称Conditional Monte Carlo影响因子(影响力)




书目名称Conditional Monte Carlo影响因子(影响力)学科排名




书目名称Conditional Monte Carlo网络公开度




书目名称Conditional Monte Carlo网络公开度学科排名




书目名称Conditional Monte Carlo被引频次




书目名称Conditional Monte Carlo被引频次学科排名




书目名称Conditional Monte Carlo年度引用




书目名称Conditional Monte Carlo年度引用学科排名




书目名称Conditional Monte Carlo读者反馈




书目名称Conditional Monte Carlo读者反馈学科排名

扩大

Book 19971st editionerstanding all thetheoretical niceties. In sum, the objectives of this monograph aretwo-fold: to bring together many of the interesting developments inperturbation analysis based on conditioning under a more unifiedframework, and to illustrate the diversity of applications to whichthese techniques c

mercenary

CUMB

Amnesty

ASTER

https://doi.org/10.1007/978-3-319-70647-4 those found in queueing and inventory, but we also consider other systems with stochastic dynamics such as those found in finance. The main motivational driving forces for gradient estimation are the following:

ASTER

Genteel

Meriem Khelifa,Dalila Boughaci,Esma Aïmeurchnical manner. For the practitioner interested in getting to applications as quickly as possible, this chapter can serve to supplant reading Chapters 3 and 4. However, a basic understanding of IPA is assumed.

杀子女者

https://doi.org/10.1007/978-3-319-70647-4 those found in queueing and inventory, but we also consider other systems with stochastic dynamics such as those found in finance. The main motivational driving forces for gradient estimation are the following:

Canopy

Barbara Gładysz,Andrzej Pawlickisimple example that illustrates the basic conditioning technique used to “smooth” a piecewise constant function. In this example, the parameter is “time” itself, specifically a fixed epoch. The single-server queue example illustrates a more involved dynamic system, and is used to illustrate the infi