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Activity Number: 384
Type: Contributed
Date/Time: Wednesday, August 1, 2007 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract - #309931
Title: Destratified Importance Sampling
Author(s): Joseph Blitzstein*+
Companies: Harvard University
Address: Statistics Dept, Cambridge, MA, 02138,
Keywords: importance sampling ; networks ; graphs ; sequential Monte Carlo
Abstract:

Importance sampling is often employed for estimation in large state spaces. In some situations though, the trial distribution is unknown or computationally difficult. We describe an approach to such problems, in which the state space is enriched to a larger space, and then importance sampling on the larger space can be used to induce efficient importance sampling estimates for the original space. As an application, we use the method to estimate average geodesic distances in various network models.

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Revised September, 2007