The clipping schemes provide good performance when there is a small discrepancy between the proposal and the tar- get functions. Moreover, they work better with high number of samples N. ClipIS outperforms ClipIS-min in most of the cases. GIS seems the better scheme with smaller N.
Importance Sampling (IS) methods approximate a targeted distribution with a set of weighted samples, drawn from a proposal distribution.
Importance Sampling (IS) methods approximate a targeted distribution with a set of weighted samples, drawn from a proposal distribution.
Overview ; event. 2018 IEEE Statistical Signal Processing Workshop (SSP) ; event place. Freiburg im Breisgau, Germany ; country. ALEMANIA ; participation category.
Importance Sampling (IS) methods approximate a targeted distribution with a set of weighted samples, drawn from a proposal distribution.
A Comparison Of Clipping Strategies For Importance Sampling. L. Martino, V. Elvira, J. Míguez, A. Artés-Rodríguez, and P. Djuric. SSP, page 558-562. IEEE ...
Title : A Comparison Of Clipping Strategies For Importance Sampling ; Author(s) : Martino, L. [Auteur] Elvira, Víctor [Auteur] Institut TELECOM/TELECOM Lille1
Bibliographic details on A Comparison Of Clipping Strategies For Importance Sampling.
Titre : A Comparison Of Clipping Strategies For Importance Sampling ; Auteur(s) : Martino, L. [Auteur] Elvira, Víctor [Auteur] Institut TELECOM/TELECOM Lille1
Feb 28, 2024 · In this work, we explore and compare three techniques that derive from importance sampling: loss reweighting, undersampling, and oversampling.