# Swift: Compiled Inference for Probabilistic Programming Languages

arXiv: https://arxiv.org/abs/1606.09242 · HTML: https://mlsanity.com/paper/1606.09242/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2016.

## Built on by (most cited first, 1 of 1)

- [Scaling Integer Arithmetic in Probabilistic Programs](https://mlsanity.com/paper/2307.13837/) (2023)

## Abstract (arXiv)

A probabilistic program defines a probability measure over its semantic structures. One common goal of probabilistic programming languages (PPLs) is to compute posterior probabilities for arbitrary models and queries, given observed evidence, using a generic inference engine. Most PPL inference engines---even the compiled ones---incur significant runtime interpretation overhead, especially for contingent and open-universe models. This paper describes Swift, a compiler for the BLOG PPL. Swift-generated code incorporates optimizations that eliminate interpretation overhead, maintain dynamic dependencies efficiently, and handle memory management for possible worlds of varying sizes. Experiments comparing Swift with other PPL engines on a variety of inference problems demonstrate speedups ranging from 12x to 326x.