Variational Inference over Nonstationary Data Streams for Exponential Family Models
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Masegosa Arredondo, Andrés Ramón![University of Almería authority University of Almería authority](/themes/Mirage2/images/autoridades/autoridad.png)
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Date
2020-11-03Abstract
In 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 smoot...
Palabra/s clave
latent variable models
nonstationary data streams
concept drift
variational inference
power priors
exponential forgetting