Likelihood Inference for Gibbs Processes in the Analysis of Spatial Point Patterns

DOI10.1111/j.1751-5823.2001.tb00481.xzbMath1213.62152OpenAlexW2029481312WikidataQ126261015 ScholiaQ126261015MaRDI QIDQ4831991

Jorge Mateu, Francisco Montes

Publication date: 3 January 2005

Published in: International Statistical Review (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1751-5823.2001.tb00481.x

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Point estimation (62F10)

Related Items (2)

Bayesian modeling on continuously marked spatial point patterns ⋮ Bayesian inference for spatially inhomogeneous pairwise interacting point processes

Uses Software

• spatial

Cites Work

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• On the rate of convergence of spatial birth-and-death processes
• Estimation of interaction potentials of spatial point patterns through the maximum likelihood procedure
• An interruptible algorithm for perfect sampling via Markov chains
• Characterization results and Markov chain Monte Carlo algorithms including exact simulation for some spatial point processes
• Estimation of Interaction Potentials of Marked Spatial Point Patterns Through the Maximum Likelihood Method
• On a General Class of Models for Interaction
• A model for clustering
• A note on Strauss's model for clustering
• Markov Point Processes
• Maximum‐likelihood estimation for constrained‐ or missing‐data models
• Statistics for Spatial Data
• Exponential spaces and counting processes
• Statistical Inference for Spatial Processes

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