Abstract
In this chapter, we derive conditional Monte Carlo estimators under the framework of the generalized semi-Markov process (GSMP). The requisite notation 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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© 1997 Springer Science+Business Media New York
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Fu, M., Hu, JQ. (1997). Conditional Monte Carlo Gradient Estimation. 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_3
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DOI: https://doi.org/10.1007/978-1-4615-6293-1_3
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-7889-1
Online ISBN: 978-1-4615-6293-1
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