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

AWL

Jan To?i?ka,Jan Jakub?v,Antonín Komendan is provided in Section 3.1 (for the GSMP framework) and near the beginning of Section 3.3 (for the gradient estimation), and the primary technical assumptions are presented in Section 3.2, where some preliminary results on infinitesimal perturbation analysis (IPA) are developed.

Meager

https://doi.org/10.1007/978-3-319-78301-7g and structural — and by conditioning on varying degrees of sample path information. In this chapter, we link our framework to the results of various other PA settings — rare perturbation analysis (RPA), discontinuous perturbation analysis (DPA), and augmented infinitesimal perturbation analysis (A

粗語

Meriem Khelifa,Dalila Boughaci,Esma A?meurchnical 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.

Endoscope

Fault-Tolerance in XJAF Agent Middleware,gradient estimation techniques. We concentrate on various examples related to a single queue, as even for this simple queueing system, there are many problems where IPA does not suffice, either because the sample performance measure is discontinuous or because the system itself has “discontinuities”

magenta

https://doi.org/10.1007/978-3-662-58464-4. When there are no set-up costs for ordering, a single parameter type of policy, such as the so-called “base-stock” policy that specifies a target level of inventory, is usually optimal. For most performance measures of interest, the sample performance function is continuous, so that IPA applies. H

palliative-care

https://doi.org/10.1007/978-3-662-58464-4In this chapter, we present examples from three other application areas:

euphoria

Other Applications,In this chapter, we present examples from three other application areas:

樹膠

https://doi.org/10.1007/978-1-4615-6293-1Internet; computer; computer science; optimization; simulation

Gleason-score

978-1-4613-7889-1Springer Science+Business Media New York 1997

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