# GP-BART: a novel Bayesian additive regression trees approach using Gaussian processes

arXiv: https://arxiv.org/abs/2204.02112 · HTML: https://mlsanity.com/paper/2204.02112/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 50 references that are in the map.
- First posted on arXiv 5 Apr 2022.

## Builds on (most cited first, 7 of 7)

- [ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R](https://mlsanity.com/paper/1508.04409/) (2015)
- [Efficiently Sampling Functions from Gaussian Process Posteriors](https://mlsanity.com/paper/2002.09309/) (2020)
- [Bayesian Regression Tree Ensembles that Adapt to Smoothness and Sparsity](https://mlsanity.com/paper/1707.09461/) (2017)
- [bartMachine: Machine Learning with Bayesian Additive Regression Trees](https://mlsanity.com/paper/1312.2171/) (2013)
- [Stochastic tree ensembles for regularized nonlinear regression](https://mlsanity.com/paper/2002.03375/) (2020)
- [The Mondrian Kernel](https://mlsanity.com/paper/1606.05241/) (2016)
- [Bayesian Additive Regression Trees with Model Trees](https://mlsanity.com/paper/2006.07493/) (2020)

## Built on by (most cited first, 7 of 7)

- [BARK: A Fully Bayesian Tree Kernel for Black-box Optimization](https://mlsanity.com/paper/2503.05574/) (2025)
- [All Emulators are Wrong, Many are Useful, and Some are More Useful Than Others: A Reproducible Comparison of Computer Model Surrogates](https://mlsanity.com/paper/2512.09060/) (2025)
- [Gaussian Process-Gated Hierarchical Mixtures of Experts](https://mlsanity.com/paper/2302.04947/) (2023)
- [Bayesian Decision Trees via Tractable Priors and Probabilistic Context-Free Grammars](https://mlsanity.com/paper/2302.07407/) (2023)
- [Variational Boosted Soft Trees](https://mlsanity.com/paper/2302.10706/) (2023)
- [Oblique Bayesian additive regression trees](https://mlsanity.com/paper/2411.08849/) (2024)
- [Dynamic Spatial Bayesian Machine Learning Model: Applications to Intergenerational Economic Mobility and Geographic Income Inequality in the United States](https://mlsanity.com/paper/2610.00072/) (2026)

## Abstract (arXiv)

The Bayesian additive regression trees (BART) model is an ensemble method extensively and successfully used in regression tasks due to its consistently strong predictive performance and its ability to quantify uncertainty. BART combines "weak" tree models through a set of shrinkage priors, whereby each tree explains a small portion of the variability in the data. However, the lack of smoothness and the absence of an explicit covariance structure over the observations in standard BART can yield poor performance in cases where such assumptions would be necessary. The Gaussian processes Bayesian additive regression trees (GP-BART) model is an extension of BART which addresses this limitation by assuming Gaussian process (GP) priors for the predictions of each terminal node among all trees. The model's effectiveness is demonstrated through applications to simulated and real-world data, surpassing the performance of traditional modeling approaches in various scenarios.