scholarly journals Correlation‐Aware Multiple Importance Sampling for Bidirectional Rendering Algorithms

2021 ◽  
Vol 40 (2) ◽  
pp. 231-238
Author(s):  
Pascal Grittmann ◽  
Iliyan Georgiev ◽  
Philipp Slusallek
Keyword(s):  
Importance Sampling ◽  
Multiple Importance Sampling

scholarly journals Drone Ground Impact Footprints with Importance Sampling: Estimation and Sensitivity Analysis

2021 ◽  
Vol 11 (9) ◽  
pp. 3871
Author(s):  
Jérôme Morio ◽  
Baptiste Levasseur ◽  
Sylvain Bertrand
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Importance Sampling ◽  
Loss Of Control ◽  
Minimum Volume ◽  
Multiple Importance Sampling ◽  
Impact Position ◽  
Engine Failure ◽  
Main Engine ◽  
Ground Impact

This paper addresses the estimation of accurate extreme ground impact footprints and probabilistic maps due to a total loss of control of fixed-wing unmanned aerial vehicles after a main engine failure. In this paper, we focus on the ground impact footprints that contains 95%, 99% and 99.9% of the drone impacts. These regions are defined here with density minimum volume sets and may be estimated by Monte Carlo methods. As Monte Carlo approaches lead to an underestimation of extreme ground impact footprints, we consider in this article multiple importance sampling to evaluate them. Then, we perform a reliability oriented sensitivity analysis, to estimate the most influential uncertain parameters on the ground impact position. We show the results of these estimations on a realistic drone flight scenario.

scholarly journals Adaptive multiple importance sampling for Gaussian processes

2017 ◽  
Vol 87 (8) ◽  
pp. 1644-1665 ◽  
Author(s):  
Xiaoyu Xiong ◽  
Václav Šmídl ◽  
Maurizio Filippone
Keyword(s):  
Importance Sampling ◽  
Gaussian Processes ◽  
Multiple Importance Sampling

Adaptive BRDF‐Oriented Multiple Importance Sampling of Many Lights

2019 ◽  
Vol 38 (4) ◽  
pp. 123-133 ◽  
Author(s):  
Yifan Liu ◽  
Kun Xu ◽  
Ling‐Qi Yan
Keyword(s):  
Importance Sampling ◽  
Multiple Importance Sampling
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