# Flexible Bayesian Nonlinear Model Configuration

arXiv: https://arxiv.org/abs/2003.02929 · HTML: https://mlsanity.com/paper/2003.02929/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2020.

## Built on by (most cited first, 5 of 5)

- [Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI](https://mlsanity.com/paper/2402.00809/) (2024)
- [Variational Inference for Bayesian Neural Networks under Model and Parameter Uncertainty](https://mlsanity.com/paper/2305.00934/) (2023)
- [Explainable Bayesian deep learning through input-skip Latent Binary Bayesian Neural Networks](https://mlsanity.com/paper/2503.10496/) (2025)
- [Reversible Genetically Modified Mode Jumping MCMC](https://mlsanity.com/paper/2110.05316/) (2021)
- [Deep Variational Inference Symbolic Regression](https://mlsanity.com/paper/2605.01067/) (2026)

## Abstract (arXiv)

Regression models are used in a wide range of applications providing a powerful scientific tool for researchers from different fields. Linear, or simple parametric, models are often not sufficient to describe complex relationships between input variables and a response. Such relationships can be better described through flexible approaches such as neural networks, but this results in less interpretable models and potential overfitting. Alternatively, specific parametric nonlinear functions can be used, but the specification of such functions is in general complicated. In this paper, we introduce a flexible approach for the construction and selection of highly flexible nonlinear parametric regression models. Nonlinear features are generated hierarchically, similarly to deep learning, but have additional flexibility on the possible types of features to be considered. This flexibility, combined with variable selection, allows us to find a small set of important features and thereby more interpretable models. Within the space of possible functions, a Bayesian approach, introducing priors for functions based on their complexity, is considered. A genetically modified mode jumping Markov chain Monte Carlo algorithm is adopted to perform Bayesian inference and estimate posterior probabilities for model averaging. In various applications, we illustrate how our approach is used to obtain meaningful nonlinear models. Additionally, we compare its predictive performance with several machine learning algorithms.