Detalles del recurso
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Project Euclid (Hosted at Cornell University Library)
Descripción: This paper proposes a new algorithm for Bayesian model determination in Gaussian graphical models under G-Wishart prior distributions. We first review recent development in sampling from G-Wishart distributions for given graphs, with a particular interest in the efficiency of the block Gibbs samplers and other competing methods. We generalize the maximum clique block Gibbs samplers to a class of flexible block Gibbs samplers and prove its convergence. This class of block Gibbs samplers substantially outperforms its competitors along a variety of dimensions. We next develop the theory and computational details of a novel Markov chain Monte Carlo sampling scheme for Gaussian graphical model determination. Our method relies on the partial analytic structure of G-Wishart distributions integrated with the exchange algorithm. Unlike existing methods, the new method requires neither proposal tuning nor evaluation of normalizing constants of G-Wishart distributions.
Autor(es): Wang, Hao - Li, Sophia Zhengzi -
Id.: 55008589
Idioma:
inglés
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Versión: 1.0
Estado: Final
Tipo: application/pdf -
Palabras clave: Exchange algorithms -
Tipo de recurso:
Text
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Tipo de Interactividad: Expositivo
Nivel de Interactividad: muy bajo
Audiencia:
Estudiante
- Profesor
- Autor
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Estructura: Atomic
Coste: no
Copyright: sí
: Copyright 2012 Institute of Mathematical Statistics
Formatos: application/pdf -
Requerimientos técnicos: Browser: Any -
Relación:
[References] 1935-7524
Fecha de contribución: 15-may-2012
Contacto:
