# Catalytic Priors: Using Synthetic Data to Specify Prior Distributions in Bayesian Analysis

arXiv: https://arxiv.org/abs/2208.14123 · HTML: https://mlsanity.com/paper/2208.14123/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2022.

## Built on by (most cited first, 3 of 3)

- [Demystifying Prediction Powered Inference](https://mlsanity.com/paper/2601.20819/) (2026)
- [Supercharging Bayesian Inference with Reliable AI-Informed Priors](https://mlsanity.com/paper/2605.09834/) (2026)
- [More accurate behavioral predictions with hybrid Bayesian-connectionist models](https://mlsanity.com/paper/2608.22154/) (2026)

## Abstract (arXiv)

Catalytic prior distributions provide general, easy-to-use, and interpretable specifications of prior distributions for Bayesian analysis. They are particularly beneficial when the observed data are inadequate to stably estimate a complex target model. A catalytic prior distribution is constructed by augmenting the observed data with synthetic data that are sampled from the predictive distribution of a simpler model estimated from the observed data. We illustrate the usefulness of the catalytic prior approach using an example from labor economics. In the example, the resulting Bayesian inference reflects many important aspects of the observed data, and the estimation accuracy and predictive performance of the inference based on the catalytic prior are superior to, or comparable to, that of other commonly used prior distributions. We further explore the connection between the catalytic prior approach and a few popular regularization methods. We expect the catalytic prior approach to be useful in many applications.