2015
Luengo, David; Martino, Luca; Elvira, Victor; Bugallo, Monica F
Bias correction for distributed Bayesian estimators Proceedings Article
En: 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp. 253–256, IEEE, Cancun, 2015, ISBN: 978-1-4799-1963-5.
Resumen | Enlaces | BibTeX | Etiquetas: Bayes methods, Big data, Distributed databases, Estimation, Probability density function, Wireless Sensor Networks
@inproceedings{Luengo2015a,
title = {Bias correction for distributed Bayesian estimators},
author = {David Luengo and Luca Martino and Victor Elvira and Monica F Bugallo},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7383784},
doi = {10.1109/CAMSAP.2015.7383784},
isbn = {978-1-4799-1963-5},
year = {2015},
date = {2015-12-01},
booktitle = {2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)},
pages = {253--256},
publisher = {IEEE},
address = {Cancun},
abstract = {Dealing with the whole dataset in big data estimation problems is usually unfeasible. A common solution then consists of dividing the data into several smaller sets, performing distributed Bayesian estimation and combining these partial estimates to obtain a global estimate. A major problem of this approach is the presence of a non-negligible bias in the partial estimators, due to the mismatch between the unknown true prior and the prior assumed in the estimation. A simple method to mitigate the effect of this bias is proposed in this paper. Essentially, the approach is based on using a reference data set to obtain a rough estimation of the parameter of interest, i.e., a reference parameter. This information is then communicated to the partial filters that handle the smaller data sets, which can thus use a refined prior centered around this parameter. Simulation results confirm the good performance of this scheme.},
keywords = {Bayes methods, Big data, Distributed databases, Estimation, Probability density function, Wireless Sensor Networks},
pubstate = {published},
tppubtype = {inproceedings}
}
Dealing with the whole dataset in big data estimation problems is usually unfeasible. A common solution then consists of dividing the data into several smaller sets, performing distributed Bayesian estimation and combining these partial estimates to obtain a global estimate. A major problem of this approach is the presence of a non-negligible bias in the partial estimators, due to the mismatch between the unknown true prior and the prior assumed in the estimation. A simple method to mitigate the effect of this bias is proposed in this paper. Essentially, the approach is based on using a reference data set to obtain a rough estimation of the parameter of interest, i.e., a reference parameter. This information is then communicated to the partial filters that handle the smaller data sets, which can thus use a refined prior centered around this parameter. Simulation results confirm the good performance of this scheme.