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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Mancini, S., Bernal, F. & Acebron, J. A. (2016). An efficient algorithm for accelerating Monte Carlo approximations of the solution to boundary value problems. Journal of Scientific Computing. 66 (2), 577-597
Exportar Referência (IEEE) 
S. Mancini et al.,  "An efficient algorithm for accelerating Monte Carlo approximations of the solution to boundary value problems", in Journal of Scientific Computing, vol. 66, no. 2, pp. 577-597, 2016
Exportar BibTeX 
@article{mancini2016_1714796486190,
	author = "Mancini, S. and Bernal, F. and Acebron, J. A.",
	title = "An efficient algorithm for accelerating Monte Carlo approximations of the solution to boundary value problems",
	journal = "Journal of Scientific Computing",
	year = "2016",
	volume = "66",
	number = "2",
	doi = "10.1007/s10915-015-0033-4",
	pages = "577-597",
	url = "http://link.springer.com/article/10.1007%2Fs10915-015-0033-4"
}
Exportar RIS 
TY  - JOUR
TI  - An efficient algorithm for accelerating Monte Carlo approximations of the solution to boundary value problems
T2  - Journal of Scientific Computing
VL  - 66
IS  - 2
AU  - Mancini, S.
AU  - Bernal, F.
AU  - Acebron, J. A.
PY  - 2016
SP  - 577-597
SN  - 0885-7474
DO  - 10.1007/s10915-015-0033-4
UR  - http://link.springer.com/article/10.1007%2Fs10915-015-0033-4
AB  - The numerical approximation of boundary value problems by means of a probabilistic representations often has the drawback that the Monte Carlo estimate of the solution is substantially biased due to the presence of the domain boundary. We introduce a scheme, which we have called the leading-term Monte Carlo regression, which seeks to remove that bias by replacing a ’cloud’ of Monte Carlo estimates—carried out at different discretization levels—for the usual single Monte Carlo estimate. The practical result of our scheme is an acceleration of the Monte Carlo method. Theoretical analysis of the proposed scheme, confirmed by numerical experiments, shows that the achieved speedup can be well over 100.
ER  -