# Automatic Rao-Blackwellization for Sequential Monte Carlo with Belief Propagation

arXiv: https://arxiv.org/abs/2312.09860 · HTML: https://mlsanity.com/paper/2312.09860/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 15 Dec 2023.

## Builds on (most cited first, 7 of 7)

- [Pyro: Deep Universal Probabilistic Programming](https://mlsanity.com/paper/1810.09538/) (2018)
- [A Differentiable Programming System to Bridge Machine Learning and Scientific Computing](https://mlsanity.com/paper/1907.07587/) (2019)
- [Design and Implementation of Probabilistic Programming Language Anglican](https://mlsanity.com/paper/1608.05263/) (2016)
- [Automated learning with a probabilistic programming language: Birch](https://mlsanity.com/paper/1810.01539/) (2018)
- [Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs](https://mlsanity.com/paper/1708.07787/) (2017)
- [Reactive Probabilistic Programming](https://mlsanity.com/paper/1908.07563/) (2019)
- [Semi-Symbolic Inference for Efficient Streaming Probabilistic Programming](https://mlsanity.com/paper/2209.07490/) (2022)

## Abstract (arXiv)

Exact Bayesian inference on state-space models~(SSM) is in general untractable, and unfortunately, basic Sequential Monte Carlo~(SMC) methods do not yield correct approximations for complex models. In this paper, we propose a mixed inference algorithm that computes closed-form solutions using belief propagation as much as possible, and falls back to sampling-based SMC methods when exact computations fail. This algorithm thus implements automatic Rao-Blackwellization and is even exact for Gaussian tree models.