Heretical Multiple Importance Sampling
| Autor: | Elvira, Víctor, Martino, Luca, Luengo, David, Bugallo, Mónica F. |
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| Rok vydání: | 2016 |
| Předmět: | |
| Zdroj: | IEEE Signal Processing Letter, Volume 23, Issue 10, October 2016 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1109/LSP.2016.2600678 |
| Popis: | Multiple Importance Sampling (MIS) methods approximate moments of complicated distributions by drawing samples from a set of proposal distributions. Several ways to compute the importance weights assigned to each sample have been recently proposed, with the so-called deterministic mixture (DM) weights providing the best performance in terms of variance, at the expense of an increase in the computational cost. A recent work has shown that it is possible to achieve a trade-off between variance reduction and computational effort by performing an a priori random clustering of the proposals (partial DM algorithm). In this paper, we propose a novel "heretical" MIS framework, where the clustering is performed a posteriori with the goal of reducing the variance of the importance sampling weights. This approach yields biased estimators with a potentially large reduction in variance. Numerical examples show that heretical MIS estimators can outperform, in terms of mean squared error (MSE), both the standard and the partial MIS estimators, achieving a performance close to that of DM with less computational cost. Comment: 8 pages, 2 figures |
| Databáze: | arXiv |
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