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

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dc.contributor.authorSbert, Mateuen_US
dc.contributor.authorHavran, Vlastimilen_US
dc.contributor.authorSzirmay-Kalos, Laszloen_US
dc.contributor.editorEitan Grinspun and Bernd Bickel and Yoshinori Dobashien_US
dc.date.accessioned2016-10-11T05:20:56Z
dc.date.available2016-10-11T05:20:56Z
dc.date.issued2016
dc.description.abstractWe reexamine in this paper the variance for the Multiple Importance Sampling (MIS) estimator for multi-sample and onesample 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 extend the analysis to include the cost of sampling. With these results in hand we find a better estimator than balance heuristic with equal count of samples. Further, we show that the variance for the one-sample model is larger or equal than for the multi-sample model, and that there are only two cases where the variance is the same. Finally, we study on four examples the difference of variances for equal count as used by Veach, our new estimator, and a recently introduced heuristic.en_US
dc.description.number7
dc.description.sectionheadersRealistic Rendering
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume35
dc.identifier.doi10.1111/cgf.13042
dc.identifier.issn1467-8659
dc.identifier.pages451-460
dc.identifier.urihttps://doi.org/10.1111/cgf.13042
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13042
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectG.3 [Computer Graphics]
dc.subjectMathematics of Computing / PROBABILITY AND STATISTICS
dc.subjectProbabilistic algorithms
dc.titleVariance Analysis of Multi-sample and One-sample Multiple Importance Samplingen_US
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