# Bayesian Experimental Design for Symbolic Discovery

arXiv: https://arxiv.org/abs/2211.15860 · HTML: https://mlsanity.com/paper/2211.15860/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 9 references that are in the map.
- First posted on arXiv 29 Nov 2022.

## Builds on (most cited first, 2 of 2)

- [AI Descartes: Combining Data and Theory for Derivable Scientific Discovery](https://mlsanity.com/paper/2109.01634/) (2021)
- [Experimental Design for Non-Parametric Correction of Misspecified Dynamical Models](https://mlsanity.com/paper/1705.00956/) (2017)

## Abstract (arXiv)

This study concerns the formulation and application of Bayesian optimal experimental design to symbolic discovery, which is the inference from observational data of predictive models taking general functional forms. We apply constrained first-order methods to optimize an appropriate selection criterion, using Hamiltonian Monte Carlo to sample from the prior. A step for computing the predictive distribution, involving convolution, is computed via either numerical integration, or via fast transform methods.