Bayesian computation for Log-Gaussian Cox processes: a comparative analysis of methods

DOI10.1080/00949655.2017.1326117OpenAlexW2613020357WikidataQ47375882 ScholiaQ47375882MaRDI QIDQ5106925

Ming Teng, Farouk S. Nathoo, Timothy D. Johnson

Publication date: 22 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1701.00857

zbMATH Keywords

variational BayesHamiltonian Monte Carlointegrated nested Laplace approximationlog-Gaussian Cox process

Mathematics Subject Classification ID

Inference from spatial processes (62M30)

Related Items (5)

High-resolution Bayesian mapping of landslide hazard with unobserved trigger event ⋮ A Statistical Review of Template Model Builder: A Flexible Tool for Spatial Modelling ⋮ Fast, Approximate Maximum Likelihood Estimation of Log-Gaussian Cox Processes ⋮ A randomized multi-index sequential Monte Carlo method ⋮ Maximum Conditional Entropy Hamiltonian Monte Carlo Sampler

Uses Software

• GMRFLib

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach
• Variational Bayesian identification and prediction of stochastic nonlinear dynamic causal models
• Spatial variation. 2nd ed
• An introduction to the theory of point processes
• Optimal scaling for various Metropolis-Hastings algorithms.
• A general method for robust Bayesian modeling
• Bayesian learning for neural networks
• Optimal tuning of the hybrid Monte Carlo algorithm
• Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
• Remarks on Some Nonparametric Estimates of a Density Function
• Approximate Bayesian inference for simple mixtures
• Handbook of Markov Chain Monte Carlo
• Joint Spatial Modeling of Recurrent Infection and Growth with Processes under Intermittent Observation
• A Kernel Method for Smoothing Point Process Data
• Log Gaussian Cox Processes
• Variational Estimation in Spatiotemporal Systems From Continuous and Point-Process Observations
• Exact Bayesian Inference in Spatiotemporal Cox Processes Driven by Multivariate Gaussian Processes
• Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods
• Gaussian Markov Random Fields
• A variational Bayes spatiotemporal model for electromagnetic brain mapping
• INLA or MCMC? A tutorial and comparative evaluation for spatial prediction in log-Gaussian Cox processes
• Meta Analysis of Functional Neuroimaging Data via Bayesian Spatial Point Processes
• Statistical Analysis and Modelling of Spatial Point Patterns

This page was built for publication: Bayesian computation for Log-Gaussian Cox processes: a comparative analysis of methods