On the Bayesian estimation for the stationary Neyman-Scott point processes.

DOI10.1007/S10492-016-0144-8zbMath1413.62134OpenAlexW2480919458MaRDI QIDQ331326

Tomáš Mrkvička, Jiří Kopecký

Publication date: 26 October 2016

Published in: Applications of Mathematics (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10338.dmlcz/145798

zbMATH Keywords

parameter estimationBayesian methodThomas processshot-noise Cox processMonte Carlo Markov chainNeyman-Scott point process

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Markov processes: estimation; hidden Markov models (62M05) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)

Uses Software

• spatstat
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Cites Work

• Distinguishing different types of inhomogeneity in Neyman-Scott point processes
• Two step estimation for Neyman-Scott point process with inhomogeneous cluster centers
• A Composite Likelihood Approach in Fitting Spatial Point Process Models
• Statistical Analysis and Modelling of Spatial Point Patterns
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• Unnamed Item
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