An acceptreject algorithm for the positive multivariate normal distribution
 Carsten Botts
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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 acceptreject algorithm is introduced in which variates from a positive multivariate normal distribution are proposed from a multivariate skewnormal distribution. This new algorithm generates an i.i.d. sample and is shown, under certain conditions, to be very efficient.
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 Title
 An acceptreject algorithm for the positive multivariate normal distribution
 Journal

Computational Statistics
Volume 28, Issue 4 , pp 17491773
 Cover Date
 20130801
 DOI
 10.1007/s0018001203772
 Print ISSN
 09434062
 Online ISSN
 16139658
 Publisher
 Springer Berlin Heidelberg
 Additional Links
 Topics
 Keywords

 Skewed normal distribution
 Gibbs sampling
 Principal components
 Industry Sectors
 Authors

 Carsten Botts ^{(1)}
 Author Affiliations

 1. Applied Physics Lab, The Johns Hopkins University, Laurel, 20723, MD, USA