@inproceedings{cb59d85293314a8baabc7ef804786a3b,
title = "A Bayesian framework for individual exposure estimation on uncertain paths",
abstract = "Current map matching and path reconstruction algorithms exhibit high success rates on dense data.We present a framework for estimating path selection probabilities from extremely sparse GPS data for the purpose of estimating a “measurement of interest” that varies with path and travel time. This work is motivated by limitations involved in applications such as environmental exposure modeling for medical patients. Our contributions are two-fold; first we propose a general Bayesian framework for path selection estimation that is applicable at both population and individual levels, and second, we provide extensive experiments on real and synthetic data that demonstrate the accuracy and robustness of the proposed algorithm and model.",
keywords = "Bayesian estimation, Exposome, Path selection probability",
author = "Matthew Horak and Bae, {Wan D.} and Shayma Alkobaisi and Sehjeong Kim and Wade Meyers",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 15th International Symposium on Web and Wireless Geographical Information Systems, W2GIS 2017 ; Conference date: 08-05-2017 Through 09-05-2017",
year = "2017",
doi = "10.1007/978-3-319-55998-8_6",
language = "English",
isbn = "9783319559971",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "83--99",
editor = "Tianzhen Wang and Xiang Li and David Brosset and Christophe Claramunt",
booktitle = "Web and Wireless Geographical Information Systems - 15th International Symposium, W2GIS 2017, Proceedings",
}