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Bayesian sparse graphical models and their mixtures
We propose Bayesian methods for Gaussian graphical models that lead to sparse and adaptively shrunk estimators of the precision (inverse covariance) matrix. Our methods are based on lasso-type regularization priors leading to parsimonious parameterization of the precision matrix, which is essential...
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| Главные авторы: | , , |
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| Формат: | Artigo |
| Язык: | Inglês |
| Опубликовано: |
2014
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| Предметы: | |
| Online-ссылка: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4059614/ https://ncbi.nlm.nih.gov/pubmed/24948842 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1002/sta4.49 |
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