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dc.contributor.authorMasegosa, Andres
dc.contributor.authorRamos-López, Dario
dc.contributor.authorSalmeron, Antonio
dc.contributor.authorLangseth, Helge
dc.contributor.authorNielsen, Thomas D.
dc.identifier.citationMathematics. 2020, 8 (11), .en_US
dc.description.abstractIn many modern data analysis problems, the available data is not static but, instead, comes in a streaming fashion. Performing Bayesian inference on a data stream is challenging for several reasons. First, it requires continuous model updating and the ability to handle a posterior distribution conditioned on an unbounded data set. Secondly, the underlying data distribution may drift from one time step to another, and the classic i.i.d. (independent and identically distributed), or data exchangeability assumption does not hold anymore. In this paper, we present an approximate Bayesian inference approach using variational methods that addresses these issues for conjugate exponential family models with latent variables. Our proposal makes use of a novel scheme based on hierarchical priors to explicitly model temporal changes of the model parameters. We show how this approach induces an exponential forgetting mechanism with adaptive forgetting rates. The method is able to capture the smoothness of the concept drift, ranging from no drift to abrupt drift. The proposed variational inference scheme maintains the computational efficiency of variational methods over conjugate models, which is critical in streaming settings. The approach is validated on four different domains (energy, finance, geolocation, and text) using four real-world data sets.en_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.titleVariational Inference over Nonstationary Data Streams for Exponential Family Modelsen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.localcodec 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (

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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal