ABC Shadow algorithm: a tool for statistical analysis of spatial patterns
comunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
comunitat-uji-handle4:
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http://dx.doi.org/10.1007/s11222-016-9682-x |
Metadata
Title
ABC Shadow algorithm: a tool for statistical analysis of spatial patternsDate
2017Publisher
Springer VerlagISSN
0960-3174; 1573-1375Bibliographic citation
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, 1-14Type
info:eu-repo/semantics/articlePublisher version
https://link.springer.com/article/10.1007/s11222-016-9682-xSubject
Abstract
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. [-]
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Statistics and Computing, 2017, vol. 27, núm 5Rights
© 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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http://rightsstatements.org/vocab/InC/1.0/
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This item appears in the folowing collection(s)
- INIT_Articles [754]
- MAT_Articles [770]