# Structured Factored Inference: A Framework for Automated Reasoning in Probabilistic Programming Languages

arXiv: https://arxiv.org/abs/1606.03298 · HTML: https://mlsanity.com/paper/1606.03298/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in World models for language agents, one of 11 directions in World models.
- Builds on 1 of its 24 references that are in the map.
- First posted on arXiv 10 Jun 2016.

## Placed in

- World models / [World models for language agents](https://mlsanity.com/wm/world-models-for-language-agents/)

## Builds on (most cited first, 1 of 1)

- [Black Box Variational Inference](https://mlsanity.com/paper/1401.0118/) (2013)

## Abstract (arXiv)

Reasoning on large and complex real-world models is a computationally difficult task, yet one that is required for effective use of many AI applications. A plethora of inference algorithms have been developed that work well on specific models or only on parts of general models. Consequently, a system that can intelligently apply these inference algorithms to different parts of a model for fast reasoning is highly desirable. We introduce a new framework called structured factored inference (SFI) that provides the foundation for such a system. Using models encoded in a probabilistic programming language, SFI provides a sound means to decompose a model into sub-models, apply an inference algorithm to each sub-model, and combine the resulting information to answer a query. Our results show that SFI is nearly as accurate as exact inference yet retains the benefits of approximate inference methods.