Bayesian models of data streams with Hierarchical Power Priors

Andrés Masegosa, Thomas Dyhre Nielsen, Helge Langseth, Darío Ramos-López, Antonio Salmerón, Anders Læsø Madsen

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

1 Citation (Scopus)

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.
Original languageEnglish
Title of host publicationProceedings of the 34th International Conference on Machine Learning
Volume70
Publication date2017
Pages2334-2343
Publication statusPublished - 2017
Event 34th International Conference on Machine Learning - Sydney, Australia
Duration: 6 Aug 201711 Aug 2017

Conference

Conference 34th International Conference on Machine Learning
CountryAustralia
CitySydney
Period06/08/201711/08/2017

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Masegosa, A., Nielsen, T. D., Langseth, H., Ramos-López, D., Salmerón, A., & Madsen, A. L. (2017). Bayesian models of data streams with Hierarchical Power Priors. In Proceedings of the 34th International Conference on Machine Learning (Vol. 70, pp. 2334-2343)
Masegosa, Andrés ; Nielsen, Thomas Dyhre ; Langseth, Helge ; Ramos-López, Darío ; Salmerón, Antonio ; Madsen, Anders Læsø. / Bayesian models of data streams with Hierarchical Power Priors. Proceedings of the 34th International Conference on Machine Learning. Vol. 70 2017. pp. 2334-2343
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Masegosa, A, Nielsen, TD, Langseth, H, Ramos-López, D, Salmerón, A & Madsen, AL 2017, Bayesian models of data streams with Hierarchical Power Priors. in Proceedings of the 34th International Conference on Machine Learning. vol. 70, pp. 2334-2343, 34th International Conference on Machine Learning, Sydney, Australia, 06/08/2017.

Bayesian models of data streams with Hierarchical Power Priors. / Masegosa, Andrés; Nielsen, Thomas Dyhre; Langseth, Helge; Ramos-López, Darío; Salmerón, Antonio; Madsen, Anders Læsø.

Proceedings of the 34th International Conference on Machine Learning. Vol. 70 2017. p. 2334-2343.

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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AU - Masegosa, Andrés

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AB - 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.

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Masegosa A, Nielsen TD, Langseth H, Ramos-López D, Salmerón A, Madsen AL. Bayesian models of data streams with Hierarchical Power Priors. In Proceedings of the 34th International Conference on Machine Learning. Vol. 70. 2017. p. 2334-2343