Backward Simulation Methods for Monte Carlo Statistical Inference

2013 ◽  
Author(s):  
Fredrik Lindsten
Keyword(s):  
Monte Carlo ◽  
Statistical Inference ◽  
Simulation Methods

Comparison of Monte Carlo simulation methods for the calculation of the nucleation barrier of argon

2006 ◽  
Vol 82 (3-4) ◽  
pp. 489-502 ◽  
Author(s):  
Antti Lauri ◽  
Joonas Merikanto ◽  
Evgeni Zapadinsky ◽  
Hanna Vehkamäki
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulation Methods ◽  
Nucleation Barrier

scholarly journals Estimating a size‐specific dose for helical head CT examinations using Monte Carlo simulation methods

2018 ◽  
Vol 46 (2) ◽  
pp. 902-912 ◽  
Author(s):  
Anthony J. Hardy ◽  
Maryam Bostani ◽  
Andrew M. Hernandez ◽  
Maria Zankl ◽  
Cynthia McCollough ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulation Methods ◽  
Head Ct

Adaptive Sampling Based on GH-Distance for Realistic Image Synthesis

2014 ◽  
Vol 998-999 ◽  
pp. 806-813
Author(s):  
Jian Wang ◽  
Qing Xu
Keyword(s):  
Monte Carlo ◽  
Computer Graphics ◽  
Adaptive Sampling ◽  
Random Noise ◽  
Image Synthesis ◽  
Simulation Methods ◽  
Path Tracing ◽  
Complex Scenes ◽  
Realistic Image ◽  
Better Than

Realistic image synthesis technology is an important part in computer graphics. Monte Carlo based light simulation methods, such as Monte Carlo path tracing, can deal with complex lighting computations for complex scenes, in the field of realistic image synthesis. Unfortunately, if the samples taken for each pixel are not enough, the generated images have a lot of random noise. Adaptive sampling is attractive to reduce image noise. This paper proposes a new GH-distance based adaptive sampling algorithm. Experimental results show that the method can perform better than other similar ones.

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