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In this paper, we propose a novel and generic family of multiple importance sampling estimators.
in Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
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Owen, A. and Zhou, Y. (2000), “Safe and Effective Importance Sampling,” Journal of the American Statistical Association
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Cappé, O., Guillin, A., Marin, J. M., and Robert, C. P. (2004), “Population Monte Carlo,” Journal of Computational and Graphical Statistics
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Cornuet, J. M., Marin, J. M., Mira, A., and Robert, C. P. (2012), “Adaptive Multiple Importance Sampling,” Scandinavian Journal of Statistics
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in Proceedings of the 13th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry
Havran, V. and Sbert, M. (2014),, Optimal Combination of Techniques in Multiple Importance Sampling · 2014
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Elvira, V., Martino, L., Luengo, D., and Bugallo, M. F. (2015), “Efficient Multiple Importance Sampling Estimators,” IEEE Signal Processing Letters
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Belzunce, F., Martinez-Riquelme, C., and Mulero, J. (2016), An Introduction to Stochastic Orders
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Elvira, V., Martino, L., Luengo, D., and Bugallo, M. F. (2016a), “Heretical Multiple Importance Sampling,” IEEE Signal Processing Letters
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in IEEE Statistical Signal Processing Workshop (SSP), 2016
Elvira, V., Martino, L., Luengo, D., and Bugallo, M. F. (2016b), “Multiple importance sampling with overlapping sets of proposals,” · 2016
Cited alongside, same era.
Sbert, M., Havran, V., and Szirmay-Kalos, L. (2016), “Variance Analysis of Multi-sample and One-sample Multiple Importance Sampling,” Computer Graphics Forum
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Bugallo, M. F., Elvira, V., Martino, L., Luengo, D., Míguez, J., and Djuric, P. M. (2017), “Adaptive Importance Sampling: The past, the present, and the future,” IEEE Signal Processing Magazine
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Elvira, V., Martino, L., Luengo, D., and Bugallo, M. F. (2017), “Improving Population Monte Carlo: Alternative Weighting and Resampling Schemes,” Signal Processing
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Martino, L., Elvira, V., Luengo, D., and Corander, J. (2017), “Layered adaptive importance sampling,” Statistics and Computing
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Sbert, M. and Havran, V. (2017), “Adaptive multiple importance sampling for general functions,” The Visual Computer
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Sbert, M., Havran, V., and Szirmay-Kalos, L. (2018a), “Multiple importance sampling revisited: breaking the bounds,” EURASIP Journal on Advances in Signal Processing
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Sbert, M. and Poch, J. (2016), “A necessary and sufficient condition for the inequality of generalized weighted means,” Journal of Inequalities and Applications
2016
Cited alongside, same era.
Sbert, M., Havran, V., Szirmay-Kalos, L., and Elvira, V. (2018b), “Multiple importance sampling characterization by weighted mean invariance,” The Visual Computer
Cited in the paper.
2018
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Elvira, V., Martino, L., Luengo, D., and Bugallo, M. F. (2019), “Generalized Multiple Importance Sampling,” Statistical Science
2019
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