Likelihood and Non‐parametric Bayesian MCMC Inference for Spatial Point Processes Based on Perfect Simulation and Path Sampling
From MaRDI portal
Publication:4455960
DOI10.1111/1467-9469.00348zbMath1034.62093OpenAlexW2018178613MaRDI QIDQ4455960
Kasper K. Berthelsen, Jesper Møller
Publication date: 16 March 2004
Published in: Scandinavian Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1111/1467-9469.00348
Inference from spatial processes (62M30) Non-Markovian processes: estimation (62M09) Nonparametric statistical resampling methods (62G09) Numerical analysis or methods applied to Markov chains (65C40)
Related Items (7)
Takacs-Fiksel Method for Stationary Marked Gibbs Point Processes ⋮ Campbell equilibrium equation and pseudo-likelihood estimation for non-hereditary Gibbs point processes ⋮ Pairwise interaction function estimation of stationary Gibbs point processes using basis expansion ⋮ A short history of Markov chain Monte Carlo: Subjective recollections from incomplete data ⋮ NON-PARAMETRIC BAYESIAN INFERENCE FOR INHOMOGENEOUS MARKOV POINT PROCESSES ⋮ Mixture formulae for shot noise weighted point processes ⋮ Fast Covariance Estimation for Innovations Computed from a Spatial Gibbs Point Process
Uses Software
Cites Work
- Unnamed Item
- Exact sampling from conditional Boolean models with applications to maximum likelihood inference
- Practical Maximum Pseudolikelihood for Spatial Point Patterns
- Perfect simulation using dominating processes on ordered spaces, with application to locally stable point processes
- Exact sampling with coupled Markov chains and applications to statistical mechanics
- Perfect Forward Simulation via Simulated Tempering
- Simulating normalizing constants: From importance sampling to bridge sampling to path sampling
- Statistical Inference for Spatial Processes
This page was built for publication: Likelihood and Non‐parametric Bayesian MCMC Inference for Spatial Point Processes Based on Perfect Simulation and Path Sampling
