# BARTSIMP: flexible spatial covariate modeling and prediction using Bayesian additive regression trees

arXiv: https://arxiv.org/abs/2309.13270 · HTML: https://mlsanity.com/paper/2309.13270/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 50 references that are in the map.
- First posted on arXiv 23 Sep 2023.

## Builds on (most cited first, 4 of 4)

- [Using satellite imagery to understand and promote sustainable development](https://mlsanity.com/paper/2010.06988/) (2020)
- [High-Dimensional Inference: Confidence Intervals, p-Values and R-Software hdi](https://mlsanity.com/paper/1408.4026/) (2014)
- [Estimating Under Five Mortality in Space and Time in a Developing World Context](https://mlsanity.com/paper/1711.00800/) (2017)
- [Finite Population Survey Sampling: An Unapologetic Bayesian Perspective](https://mlsanity.com/paper/2306.10635/) (2023)

## Abstract (arXiv)

Prediction is a classic challenge in spatial statistics and the inclusion of spatial covariates can greatly improve predictive performance when incorporated into a model with latent spatial effects. It is desirable to develop flexible regression models that allow for nonlinearities and interactions in the covariate specification. Existing machine learning approaches that allow for spatial dependence in the residuals fail to provide reliable uncertainty estimates. In this paper, we investigate the combination of a Gaussian process spatial model with a Bayesian Additive Regression Tree (BART) model. The computational burden of the approach is reduced by combining Markov chain Monte Carlo (MCMC) with the Integrated Nested Laplace Approximation (INLA) technique. We study the performance of the method first via simulation. We then use the model to predict anthropometric responses in Kenya, with the data collected via a complex sampling design. In particular, household survey data are collected via stratified two-stage unequal probability cluster sampling, which requires special care when modeled.