Abstract
The subject of this book is gradient estimation within a stochastic setting. The primary backdrop is that of stochastic discrete-event systems such as 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:
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Sensitivity Analysis — estimating the sensitivity of performance measures of interest to various parameters of the system, whether they be “input” parameters to the models such as parameters of the underlying driving distributions, or “decision” parameters;
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Stochastic Optimization — determining the settings of various “decision” parameters to optimize some performance measure of interest.
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© 1997 Springer Science+Business Media New York
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Fu, M., Hu, JQ. (1997). Introduction. In: Conditional Monte Carlo. The Springer International Series in Engineering and Computer Science, vol 392. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-6293-1_1
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DOI: https://doi.org/10.1007/978-1-4615-6293-1_1
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-7889-1
Online ISBN: 978-1-4615-6293-1
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