UAI 2021poster28 citations
Optimized auxiliary particle filters: adapting mixture proposals via convex optimization
Nicola Branchini, Víctor Elvira
Abstract
Auxiliary particle filters (APFs) are a class of sequential Monte Carlo (SMC) methods for Bayesian inference in state-space models. In their original derivation, APFs operate in an extended state space using an auxiliary variable to improve inference. In this work, we propose
BibTeX
@InProceedings{pmlr-v161-branchini21a,
title = {Optimized auxiliary particle filters: adapting mixture proposals via convex optimization},
author = {Branchini, Nicola and Elvira, V\'ictor},
booktitle = {Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence},
pages = {1289--1299},
year = {2021},
editor = {de Campos, Cassio and Maathuis, Marloes H.},
volume = {161},
series = {Proceedings of Machine Learning Research},
month = {27--30 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v161/branchini21a/branchini21a.pdf},
url = {https://proceedings.mlr.press/v161/branchini21a.html},
abstract = {Auxiliary particle filters (APFs) are a class of sequential Monte Carlo (SMC) methods for Bayesian inference in state-space models. In their original derivation, APFs operate in an extended state space using an auxiliary variable to improve inference. In this work, we propose