@inproceedings{4a6b3f18d7ec457790f3620566b0b06c,
title = "Sequential Monte Carlo methods under model uncertainty",
abstract = "We propose a Sequential Monte Carlo (SMC) method for filtering and prediction of time-varying signals under model uncertainty. Instead of resorting to model selection, we fuse the information from the considered models within the proposed SMC method. We achieve our goal by dynamically adjusting the resampling step according to the posterior predictive power of each model, which is updated sequentially as we observe more data. The method allows the models with better predictive powers to explore the state space with more resources than models lacking predictive power. This is done autonomously and dynamically within the SMC method. We show the validity of the presented method by evaluating it on an illustrative application.",
keywords = "Resampling, Sequential Monte Carlo, dynamic model averaging, information fusion, particle filtering",
author = "Inigo Urteaga and Bugallo, \{Monica F.\} and Djuric, \{Petar M.\}",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 19th IEEE Statistical Signal Processing Workshop, SSP 2016 ; Conference date: 25-06-2016 Through 29-06-2016",
year = "2016",
month = aug,
day = "24",
doi = "10.1109/SSP.2016.7551747",
language = "English",
series = "IEEE Workshop on Statistical Signal Processing Proceedings",
publisher = "IEEE Computer Society",
booktitle = "2016 19th IEEE Statistical Signal Processing Workshop, SSP 2016",
}