R · Open source
gaussian-process
Full Bayesian Gaussian Process Spatial Models
An R implementation of Gaussian process regression for spatial data using Gibbs sampling and adaptive Metropolis-Hastings.
Overview
This project fits hierarchical Gaussian process models to spatial data. Regression effects, process variance, measurement error, and the spatial range are represented separately in the model.
The sampler uses conjugate updates where they are available and adaptive Metropolis-Hastings for the remaining parameters. The example includes trace plots and credible intervals for a simulated spatial process.
Technical highlights
- Exponential and Gaussian spatial correlation kernels
- Adaptive Metropolis-Hastings tuning
- Gibbs sampling for regression coefficients
- Posterior spatial effects and credible intervals
Topics
Gaussian processesSpatial statisticsMarkov chain Monte CarloBayesian regressionR