Bayesian inference for spatially inhomogeneous pairwise interacting point processes

DOI10.1016/j.csda.2004.04.008zbMath1429.62424OpenAlexW1989899286MaRDI QIDQ957172

Matthew A. Bognar

Publication date: 26 November 2008

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2004.04.008

zbMATH Keywords

importance samplingMetropolis-Hastings algorithmBayesian inferencereversible jump MCMCGibbs point processinhomogeneous pairwise interacting point processspatial point pattern

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Bayesian inference (62F15) Monte Carlo methods (65C05) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)

Related Items (3)

Use of SAMC for Bayesian analysis of statistical models with intractable normalizing constants ⋮ Bayesian analysis of a Gibbs hard-core point pattern model with varying repulsion range ⋮ Bayesian modeling on continuously marked spatial point patterns

Uses Software

• spatial

Cites Work

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• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• Likelihood estimation of soft-core interaction potentials for Gibbsian point patterns
• Estimation of interaction potentials of spatial point patterns through the maximum likelihood procedure
• On Monte Carlo methods for estimating ratios of normalizing constants
• Inference from iterative simulation using multiple sequences
• Bayesian smoothing in the estimation of the pair potential function of Gibbs point processes
• Statistical Inference for Spatial Processes
• Estimator for the pair–potential of a gibbsian point process
• A nonparametric estimator for pairwise-interaction point processes
• Point process limits of lattice processes
• A model for clustering
• A note on Strauss's model for clustering
• Computing Dirichlet Tessellations in the Plane
• Non-parametric Bayesian Estimation of a Spatial Poisson Intensity
• 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
• Likelihood Inference for Gibbs Processes in the Analysis of Spatial Point Patterns
• On Parameter Estimation for Pairwise Interaction Point Processes
• Equation of State Calculations by Fast Computing Machines
• Exponential spaces and counting processes
• Monte Carlo sampling methods using Markov chains and their applications
• Simulating normalizing constants: From importance sampling to bridge sampling to path sampling

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