High-dimensional generation of Bernoulli random vectors
The objective of this paper is to explore different modeling strategies to generate high-dimensional Bernoulli vectors. We discuss the multivariate Bernoulli (MB) distribution, probe its properties and examine three models for generating random vectors. A latent multivariate normal model whose bivariate distributions are approximated with Plackett distributions with univariate normal distributions is presented. A conditional mean model is examined where the conditional probability of success depends on previous history of successes. A mixture of beta distributions is also presented that expresses the probability of the MB vector as a product of correlated binary random variables. Each method has a domain of effectiveness. The latent model offers unpatterned correlation structures while the conditional mean and the mixture model provide computational feasibility for high-dimensional generation of MB vectors.
Year of publication: |
2011
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Authors: | Modarres, Reza |
Published in: |
Statistics & Probability Letters. - Elsevier, ISSN 0167-7152. - Vol. 81.2011, 8, p. 1136-1142
|
Publisher: |
Elsevier |
Subject: | Binary Mixture Latent Dependence Network |
Saved in:
Online Resource
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