High-dimensional Filtering using Nested Sequential Monte Carlo
Computation
2016-12-30 v1 Machine Learning
Abstract
Sequential Monte Carlo (SMC) methods comprise one of the most successful approaches to approximate Bayesian filtering. However, SMC without good proposal distributions struggle in high dimensions. We propose nested sequential Monte Carlo (NSMC), a methodology that generalises the SMC framework by requiring only approximate, properly weighted, samples from the SMC proposal distribution, while still resulting in a correct SMC algorithm. This way we can exactly approximate the locally optimal proposal, and extend the class of models for which we can perform efficient inference using SMC. We show improved accuracy over other state-of-the-art methods on several spatio-temporal state space models.
Cite
@article{arxiv.1612.09162,
title = {High-dimensional Filtering using Nested Sequential Monte Carlo},
author = {Christian A. Naesseth and Fredrik Lindsten and Thomas B. Schön},
journal= {arXiv preprint arXiv:1612.09162},
year = {2016}
}