Bayesian Models of Data Streams with Hierarchical Power Priors
Andrés Masegosa, Thomas D. Nielsen, Helge Langseth, Darı́o Ramos-López, Antonio Salmerón, Anders L. Madsen
Abstract
Making inferences from data streams is a pervasive problem in many modern data analysis applications. But it requires to address the problem of continuous model updating, and adapt to changes or drifts in the underlying data generating distribution. In this paper, we approach these problems from a Bayesian perspective covering general conjugate exponential models. Our proposal makes use of non-conjugate hierarchical priors to explicitly model temporal changes of the model parameters. We also derive a novel variational inference scheme which overcomes the use of non-conjugate priors while maintaining the computational efficiency of variational methods over conjugate models. The approach is validated on three real data sets over three latent variable models.
BibTeX
@InProceedings{pmlr-v70-masegosa17a,
title = {{B}ayesian Models of Data Streams with Hierarchical Power Priors},
author = {Andr{\'e}s Masegosa and Thomas D. Nielsen and Helge Langseth and Dar\'{\i}o Ramos-L{\'o}pez and Antonio Salmer{\'o}n and Anders L. Madsen},
booktitle = {Proceedings of the 34th International Conference on Machine Learning},
pages = {2334--2343},
year = {2017},
editor = {Precup, Doina and Teh, Yee Whye},
volume = {70},
series = {Proceedings of Machine Learning Research},
month = {06--11 Aug},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v70/masegosa17a/masegosa17a.pdf},
url = {https://proceedings.mlr.press/v70/masegosa17a.html},
abstract = {Making inferences from data streams is a pervasive problem in many modern data analysis applications. But it requires to address the problem of continuous model updating, and adapt to changes or drifts in the underlying data generating distribution. In this paper, we approach these problems from a Bayesian perspective covering general conjugate exponential models. Our proposal makes use of non-conjugate hierarchical priors to explicitly model temporal changes of the model parameters. We also derive a novel variational inference scheme which overcomes the use of non-conjugate priors while maintaining the computational efficiency of variational methods over conjugate models. The approach is validated on three real data sets over three latent variable models.}
}