# Deployable probabilistic programming

arXiv: https://arxiv.org/abs/1906.11199 · HTML: https://mlsanity.com/paper/1906.11199/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2019.

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

- [Control-Data Separation and Logical Condition Propagation for Efficient Inference on Probabilistic Programs](https://mlsanity.com/paper/2101.01502/) (2021)
- [How To Train Your Program: a Probabilistic Programming Pattern for Bayesian Learning From Data](https://mlsanity.com/paper/2105.03650/) (2021)
- [Bob and Alice Go to a Bar: Reasoning About Future With Probabilistic Programs](https://mlsanity.com/paper/2108.03834/) (2021)

## Abstract (arXiv)

We propose design guidelines for a probabilistic programming facility suitable for deployment as a part of a production software system. As a reference implementation, we introduce Infergo, a probabilistic programming facility for Go, a modern programming language of choice for server-side software development. We argue that a similar probabilistic programming facility can be added to most modern general-purpose programming languages. Probabilistic programming enables automatic tuning of program parameters and algorithmic decision making through probabilistic inference based on the data. To facilitate addition of probabilistic programming capabilities to other programming languages, we share implementation choices and techniques employed in development of Infergo. We illustrate applicability of Infergo to various use cases on case studies, and evaluate Infergo's performance on several benchmarks, comparing Infergo to dedicated inference-centric probabilistic programming frameworks.