Efficient sampling from truncated bivariate Gaussians via Box-Muller transformation Articles uri icon

publication date

  • November 2012

start page

  • 1533

end page

  • 1534

issue

  • 24

volume

  • 48

International Standard Serial Number (ISSN)

  • 0013-5194

Electronic International Standard Serial Number (EISSN)

  • 1350-911X

abstract

  • 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.

keywords

  • gaussian distribution; sampling methods