By Michael C. Fu, Jian-Qiang Hu
Conditional Monte Carlo: Gradient Estimation and OptimizationApplications offers with quite a few gradient estimation strategies of perturbation research in keeping with using conditional expectation. the first surroundings is discrete-event stochastic simulation. This booklet offers purposes to queueing and stock, and to different various components corresponding to monetary derivatives, pricing and statistical quality controls. To researchers already within the region, this ebook deals a unified point of view and competently summarizes the cutting-edge. To researchers new to the world, this booklet bargains a extra systematic and obtainable technique of figuring out the concepts with no need to scour throughout the significant literature and examine a brand new set of notation with every one paper. To practitioners, this booklet offers a couple of different software parts that makes the instinct available with no need to completely decide to knowing all of the theoretical niceties. In sum, the targets of this monograph are two-fold: to assemble the various attention-grabbing advancements in perturbation research in line with conditioning below a extra unified framework, and to demonstrate the variety of purposes to which those concepts might be utilized.
Conditional Monte Carlo: Gradient Estimation and OptimizationApplications is acceptable as a secondary textual content for graduate point classes on stochastic simulations, and as a reference for researchers and practitioners in industry.
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