# Loss-based prior for CART and BART models

arXiv: https://arxiv.org/abs/2404.00359 · HTML: https://mlsanity.com/paper/2404.00359/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2024.

## Built on by (most cited first, 1 of 1)

- [Bayesian model selection of vine copulas: a loss-based perspective](https://mlsanity.com/paper/2606.21512/) (2026)

## Abstract (arXiv)

We present a novel prior for tree topology within Bayesian Additive Regression Trees (BART) models. This approach quantifies the hypothetical loss in information and the loss due to complexity associated with choosing the wrong tree structure. The resulting prior distribution is compellingly geared toward sparsity, a critical feature considering BART models' tendency to overfit. Our method incorporates prior knowledge into the distribution via two parameters that govern the tree's depth and balance between its left and right branches. Additionally, we propose a default calibration for these parameters, offering an objective version of the prior. We demonstrate our method's efficacy on both simulated and real datasets.