# Learning Probabilistic Programs

arXiv: https://arxiv.org/abs/1407.2646 · HTML: https://mlsanity.com/paper/1407.2646/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2014.

## Built on by (most cited first, 2 of 2)

- [How To Train Your Program: a Probabilistic Programming Pattern for Bayesian Learning From Data](https://mlsanity.com/paper/2105.03650/) (2021)
- [Efficient Incremental Belief Updates Using Weighted Virtual Observations](https://mlsanity.com/paper/2402.06940/) (2024)

## Abstract (arXiv)

We develop a technique for generalising from data in which models are samplers represented as program text. We establish encouraging empirical results that suggest that Markov chain Monte Carlo probabilistic programming inference techniques coupled with higher-order probabilistic programming languages are now sufficiently powerful to enable successful inference of this kind in nontrivial domains. We also introduce a new notion of probabilistic program compilation and show how the same machinery might be used in the future to compile probabilistic programs for efficient reusable predictive inference.