Modifications of the standard vector Monte Carlo estimate for characteristics analysis of scattered polarized radiation
- Authors: Mikhailov G.A.1,2, Prigarin S.M.1,2, Rozhenko S.A.1
 - 
							Affiliations: 
							
- Institute of Computational Mathematics and Mathematical Geophysics, Siberian Branch
 - Novosibirsk State University
 
 - Issue: Vol 96, No 2 (2017)
 - Pages: 461-464
 - Section: Mathematics
 - URL: https://journals.rcsi.science/1064-5624/article/view/225372
 - DOI: https://doi.org/10.1134/S1064562417050143
 - ID: 225372
 
Cite item
Abstract
There are two versions of weighted vector algorithms for the statistical modeling of polarized radiative transfer: a “standard” one, which is convenient for parametric analysis of results, and an “adaptive” one, which ensures finite variances of estimates. The application of the adaptive algorithm is complicated by the necessity of modeling the previously unknown transition density. An optimal version of the elimination algorithm used in this case is presented in this paper. A new combined algorithm with a finite variance and an algorithm with a mixed transition density are constructed. The comparative efficiency of the latter is numerically studied as applied to radiative transfer with a molecular scattering matrix.
About the authors
G. A. Mikhailov
Institute of Computational Mathematics and Mathematical Geophysics, Siberian Branch; Novosibirsk State University
							Author for correspondence.
							Email: gam@sscc.ru
				                					                																			                												                	Russian Federation, 							Novosibirsk, 630090; Novosibirsk, 630090						
S. M. Prigarin
Institute of Computational Mathematics and Mathematical Geophysics, Siberian Branch; Novosibirsk State University
														Email: gam@sscc.ru
				                					                																			                												                	Russian Federation, 							Novosibirsk, 630090; Novosibirsk, 630090						
S. A. Rozhenko
Institute of Computational Mathematics and Mathematical Geophysics, Siberian Branch
														Email: gam@sscc.ru
				                					                																			                												                	Russian Federation, 							Novosibirsk, 630090						
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