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Efficient sampling from truncated bivariate Gaussians via Box-Muller transformation

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URI: http://hdl.handle.net/10016/16644
ISSN: 0013-5194
DOI: 10.1049/el.2012.2816
UXXI: AR/0000011684
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efficient_martino_luengo_miguez_EL_2012_ps.pdf (235.31 KB)
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2012-11
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IEEE
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Many practical simulation tasks demand procedures to draw samples efficiently from multivariate truncated Gaussian distributions. Introduced is a novel rejection approach, based on the Box-Muller transformation, to generate samples from a truncated bivariate Gaussian density with an arbitrary support. Furthermore, for an important class of support regions the new method allows exact sampling to be achieved, thus becoming the most efficient approach possible.Many practical simulation tasks demand procedures to draw samples efficiently from multivariate truncated Gaussian distributions. Introduced is a novel rejection approach, based on the Box-Muller transformation, to generate samples from a truncated bivariate Gaussian density with an arbitrary support. Furthermore, for an important class of support regions the new method allows exact sampling to be achieved, thus becoming the most efficient approach possible.
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Gaussian distribution, Sampling methods
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Electronics Letters , vol.48, no.24, pp.1533-1534
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