Automatic Rao-Blackwellization for Sequential Monte Carlo with Belief Propagation
Abstract
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.
Cite
Text
Azizian et al. "Automatic Rao-Blackwellization for Sequential Monte Carlo with Belief Propagation." ICML 2023 Workshops: SPIGM, 2023.Markdown
[Azizian et al. "Automatic Rao-Blackwellization for Sequential Monte Carlo with Belief Propagation." ICML 2023 Workshops: SPIGM, 2023.](https://mlanthology.org/icmlw/2023/azizian2023icmlw-automatic/)BibTeX
@inproceedings{azizian2023icmlw-automatic,
title = {{Automatic Rao-Blackwellization for Sequential Monte Carlo with Belief Propagation}},
author = {Azizian, Waïss and Baudart, Guillaume and Lelarge, Marc},
booktitle = {ICML 2023 Workshops: SPIGM},
year = {2023},
url = {https://mlanthology.org/icmlw/2023/azizian2023icmlw-automatic/}
}