# Sequential Monte Carlo for Graphical Models

arXiv: https://arxiv.org/abs/1402.0330 · HTML: https://mlsanity.com/paper/1402.0330/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2014.

## Built on by (most cited first, 4 of 4)

- [Critic Sequential Monte Carlo](https://mlsanity.com/paper/2205.15460/) (2022)
- [Nested Variational Inference](https://mlsanity.com/paper/2106.11302/) (2021)
- [Sequential Monte Carlo Learning for Time Series Structure Discovery](https://mlsanity.com/paper/2307.09607/) (2023)
- [Planning with Language and Generative Models: Toward General Reward-Guided Wireless Network Design](https://mlsanity.com/paper/2602.00357/) (2026)

## Abstract (arXiv)

We propose a new framework for how to use sequential Monte Carlo (SMC) algorithms for inference in probabilistic graphical models (PGM). Via a sequential decomposition of the PGM we find a sequence of auxiliary distributions defined on a monotonically increasing sequence of probability spaces. By targeting these auxiliary distributions using SMC we are able to approximate the full joint distribution defined by the PGM. One of the key merits of the SMC sampler is that it provides an unbiased estimate of the partition function of the model. We also show how it can be used within a particle Markov chain Monte Carlo framework in order to construct high-dimensional block-sampling algorithms for general PGMs.