Bayesian computation for Log-Gaussian Cox processes: a comparative analysis of methods
From MaRDI portal
Publication:5106925
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
variational BayesHamiltonian Monte Carlointegrated nested Laplace approximationlog-Gaussian Cox process
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
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