Conditional Monte Carlo pp 133-180 | Cite as
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Chapter
Abstract
In the previous chapter, we strove to make our framework as general as possible by including the two principal types of parameters of interest — timing 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 (APA) — and to estimators derived via the likelihood ratio (LR) method and the weak derivative (WD) approach.
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© Springer Science+Business Media New York 1997