caa a22 4500 445359897 CHVBK 20180317142912.0 cr unu---uuuuu 170323e20110601xx s 000 0 eng 10.1007/s10100-010-0166-3 doi (NATIONALLICENCE)springer-10.1007/s10100-010-0166-3 Deák István Department of Computer Science, Corvinus University of Budapest, IX. Fővám tér 8, 1093, Budapest, Hungary aut Efficiency of Monte Carlo computations in very high dimensional spaces [Elektronische Daten] [István Deák] A standard measure for comparing different Monte Carlo estimators is the efficiency, which generally thought to be declining with increasing the number of dimensions. Here we give some numerical examples, ranging from one-hundred to one-thousand dimensional integration problems, that contradict this belief. Monte Carlo integrations carried out in one-thousand dimensional spaces is the other nontrivial result reported here. The examples concern the computation of the probabilities of convex sets (polyhedra and hyperellipsoids) in case of multidimensional normal probabilities. Springer-Verlag, 2010 Multidimensional normal distribution nationallicence Monte Carlo methods nationallicence Probabilities of convex sets nationallicence Efficiency of estimators nationallicence Comparison of performances nationallicence Central European Journal of Operations Research Springer-Verlag 19/2(2011-06-01), 177-189 1435-246X 19:2<177 2011 19 10100 https://doi.org/10.1007/s10100-010-0166-3 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10100-010-0166-3 text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- Deák István Department of Computer Science, Corvinus University of Budapest, IX. Fővám tér 8, 1093, Budapest, Hungary aut NATIONALLICENCE 773 0- Central European Journal of Operations Research Springer-Verlag 19/2(2011-06-01), 177-189 1435-246X 19:2<177 2011 19 10100 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer