Variance Analysis of Multi-sample and One-sample Multiple Importance Sampling

We reexamine in this paper the variance for the Multiple Importance Sampling (MIS) estimator for multi‐sample and one‐sample model. As a result of our analysis we can obtain the optimal estimator for the multi‐sample model for the case where the weights do not depend on the count of samples. We exte...

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Bibliographic Details
Published in:	Computer graphics forum Vol. 35; no. 7; pp. 451 - 460
Main Authors:	Sbert, M., Havran, V., Szirmay-Kalos, L.
Format:	Journal Article
Language:	English
Published:	Oxford Blackwell Publishing Ltd 01.10.2016
Subjects:	Analysis Categories and Subject Descriptors (according to ACM CCS) Computer graphics Counting Estimating techniques Estimators G.3 [Computer Graphics]: Mathematics of Computing / PROBABILITY AND STATISTICS-Probabilistic algorithms global illumination Heuristic Importance sampling Monte Carlo multiple importance sampling Optimization rendering equation analysis Sampling Studies Variance Variance analysis
ISSN:	0167-7055, 1467-8659
Online Access:	Get full text
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