Bayesian analysis of a Gibbs hard-core point pattern model with varying repulsion range
DOI10.1016/j.csda.2012.08.014zbMath1471.62168OpenAlexW2047092934WikidataQ110224614 ScholiaQ110224614MaRDI QIDQ1621330
Publication date: 8 November 2018
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2012.08.014
Bayesian analysisinhomogeneousGaussian process regularisationhard-core point processSand Martin's nests
Computational methods for problems pertaining to statistics (62-08) Inference from spatial processes (62M30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)
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- An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach
- Bayesian inference for spatially inhomogeneous pairwise interacting point processes
- Perfect simulation for marked point processes
- Pseudolikelihood for exponential family models of spatial point processes
- Inhomogeneous Markov point processes by transformation
- Packing densities and simulated tempering for hard core Gibbs point processes
- A toolbox for fitting complex spatial point process models using integrated nested Laplace approximation (INLA)
- NON-PARAMETRIC BAYESIAN INFERENCE FOR INHOMOGENEOUS MARKOV POINT PROCESSES
- Handbook of Spatial Statistics
- Inhomogeneous spatial point processes by location-dependent scaling
- Markov Point Processes and Their Applications
- Practical Maximum Pseudolikelihood for Spatial Point Patterns
- Perfect simulation using dominating processes on ordered spaces, with application to locally stable point processes
- Gaussian Markov Random Fields
- Random patterns of nonoverlapping convex grains
- Statistical Inference for Transformation Inhomogeneous Point Processes
- Existence of ‘nearest-neighbour’ spatial Gibbs models
- Statistical Analysis and Modelling of Spatial Point Patterns
- Inconsistent Estimation and Asymptotically Equal Interpolations in Model-Based Geostatistics
- An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants
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