Bayesian Covariance Matrix Estimation using a Mixture of Decomposable
Graphical Models
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Bayesian Covariance Matrix Estimation using a Mixture of Decomposable
Graphical Models
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| Id. |
25664908 |
| Titulo |
Bayesian Covariance Matrix Estimation using a Mixture of Decomposable
Graphical Models |
| Autor(es) |
Armstrong, Helen
Carter, Christopher K.
Wong, Kevin F.
Kohn, Robert
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| Localización |
http://arxiv.org/abs/0706.1287
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| Versión |
1.0 |
| Estado |
Final
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| Descripción |
A Bayesian approach is used to estimate the covariance matrix of Gaussian
data. Ideas from Gaussian graphical models and model selection are used to
construct a prior for the covariance matrix that is a mixture over all
decomposable graphs. For this prior the probability of each graph size is
specified by the user and graphs of equal size are assigned equal probability.
Most previous approaches assume that all graphs are equally probable. We show
empirically that the prior that assigns equal probability over graph sizes
outperforms the prior that assigns equal probability over all graphs, both in
identifying the correct decomposable graph and in more efficiently estimating
the covariance matrix.
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| Palabras clave |
Statistics - Methodology
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| Tipo de recurso |
Texto Narrativo
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| Tipo de Interactividad |
Expositivo
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| Nivel de Interactividad |
muy bajo
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| Audiencia |
Estudiante
Profesor
Autor
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| Estructura |
Atomic |
| Coste |
no
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| Copyright |
sí
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| Requerimientos técnicos |
Browser: Any
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| Fecha de contribución |
26-jun-2007 |
| Contacto |
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