An accept-reject algorithm for the positive multivariate normal distribution
The need to simulate from a positive multivariate normal distribution arises in several settings, specifically in Bayesian analysis. A variety of algorithms can be used to sample from this distribution, but most of these algorithms involve Gibbs sampling. Since the sample is generated from a Markov chain, the user has to account for the fact that sequential draws in the sample depend on one another and that the sample generated only follows a positive multivariate normal distribution asymptotically. The user would not have to account for such issues if the sample generated was i.i.d. In this paper, an accept-reject algorithm is introduced in which variates from a positive multivariate normal distribution are proposed from a multivariate skew-normal distribution. This new algorithm generates an i.i.d. sample and is shown, under certain conditions, to be very efficient. Copyright Springer-Verlag Berlin Heidelberg 2013
Year of publication: |
2013
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Authors: | Botts, Carsten |
Published in: |
Computational Statistics. - Springer. - Vol. 28.2013, 4, p. 1749-1773
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Publisher: |
Springer |
Subject: | Skewed normal distribution | Gibbs sampling | Principal components |
Saved in:
Online Resource
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