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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Conditional Monte Carlo Gradient Estimation,n 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.

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Book 19971st editions based on the use ofconditional expectation. The primary setting is discrete-eventstochastic simulation. This book presents applications to queueing andinventory, and to other diverse areas such as financial derivatives,pricing and statistical quality control. To researchers already in thearea, thi

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Barbara Gładysz,Andrzej Pawlickifficulty. In this example, the parameter appears in the distribution of the underlying stochastic processes. The (., .) inventory example illustrates the use of conditional Monte Carlo in cases where the parameter is a structural parameter.

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Three Extended Examples,fficulty. In this example, the parameter appears in the distribution of the underlying stochastic processes. The (., .) inventory example illustrates the use of conditional Monte Carlo in cases where the parameter is a structural parameter.

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https://doi.org/10.1007/978-3-319-78301-7 other PA settings — rare perturbation analysis (RPA), discontinuous perturbation analysis (DPA), and augmented infinitesimal perturbation analysis (APA) — and to estimators derived via the likelihood ratio (LR) method and the weak derivative (WD) approach.

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Links to Other Settings, other PA settings — rare perturbation analysis (RPA), discontinuous perturbation analysis (DPA), and augmented infinitesimal perturbation analysis (APA) — and to estimators derived via the likelihood ratio (LR) method and the weak derivative (WD) approach.