Perfect simulation for marked point processes
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References listed on IDEAS
- A. Mira & J. Møller & G. O. Roberts, 2001. "Perfect slice samplers," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 593-606.
- Ferrari, Pablo A. & Fernández, Roberto & Garcia, Nancy L., 2002. "Perfect simulation for interacting point processes, loss networks and Ising models," Stochastic Processes and their Applications, Elsevier, vol. 102(1), pages 63-88, November.
- J. Møller, 1999. "Perfect simulation of conditionally specified models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 251-264.
- M. N. M. Van Lieshout & R. S. Stoica, 2003. "The Candy model: properties and inference," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 57(2), pages 177-206, May.
- Kendall, Wilfrid S. & Montana, Giovanni, 2002. "Small sets and Markov transition densities," Stochastic Processes and their Applications, Elsevier, vol. 99(2), pages 177-194, June.
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Cited by:
- Rajala, T. & Penttinen, A., 2014. "Bayesian analysis of a Gibbs hard-core point pattern model with varying repulsion range," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 530-541.
- Radu S. Stoica & Vicent J. Martínez & Enn Saar, 2007. "A three‐dimensional object point process for detection of cosmic filaments," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 56(4), pages 459-477, August.
- Nicolas Picard & Avner Bar‐Hen & Frédéric Mortier & Joël Chadœuf, 2009. "The Multi‐scale Marked Area‐interaction Point Process: A Model for the Spatial Pattern of Trees," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(1), pages 23-41, March.
- Khadidja Henni & Pierre-Yves Louis & Brigitte Vannier & Ahmed Moussa, 2020. "Is-ClusterMPP: clustering algorithm through point processes and influence space towards high-dimensional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(3), pages 543-570, September.
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