ABC Shadow algorithm: a tool for statistical analysis of spatial patterns
Impacto
Scholar |
Otros documentos de la autoría: Stoica, Radu; PHILIPPE, Anne; Gregori, Pablo; Mateu, Jorge
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
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http://dx.doi.org/10.1007/s11222-016-9682-x |
Metadatos
Título
ABC Shadow algorithm: a tool for statistical analysis of spatial patternsFecha de publicación
2017Editor
Springer VerlagISSN
0960-3174; 1573-1375Cita bibliográfica
Stoica, R. S., Philippe, A., Gregori, P., & Mateu, J. (2015). ABC Shadow algorithm: a tool for statistical analysis of spatial patterns. Statistics and Computing, 2017, vol. 27, núm 5, 1-14Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://link.springer.com/article/10.1007/s11222-016-9682-xPalabras clave / Materias
Resumen
This paper presents an original ABC algorithm, ABC Shadow, that can be applied to sample posterior densities that are continuously differentiable. The proposed algorithm solves the main condition to be fulfilled by ... [+]
This paper presents an original ABC algorithm, ABC Shadow, that can be applied to sample posterior densities that are continuously differentiable. The proposed algorithm solves the main condition to be fulfilled by any ABC algorithm, in order to be useful in practice. This condition requires enough samples in the parameter space region, induced by the observed statistics. The algorithm is tuned on the posterior of a Gaussian model which is entirely known, and then, it is applied for the statistical analysis of several spatial patterns. These patterns are issued or assumed to be outcomes of point processes. The considered models are: Strauss, Candy and area-interaction. [-]
Publicado en
Statistics and Computing, 2017, vol. 27, núm 5Derechos de acceso
© Springer Science+Business Media New York 2016. "The final publication is available at Springer via http://dx.doi.org/10.1007/s11222-016-9682-x"
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