Variational Inference over Nonstationary Data Streams for Exponential Family Models

Variational Inference over Nonstationary Data Streams for Exponential Family Models

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 condi...

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Bibliographic Details
Main Authors: Masegosa Arredondo, Andrés Ramón, Ramos López, Darío, Salmerón Cerdán, Antonio, Langseth, Helge, Nielsen, Thomas Dyhre
Format: info:eu-repo/semantics/article
Language:English
Published: MDPI 2023
Subjects:
latent variable models
nonstationary data streams
concept drift
variational inference
power priors
exponential forgetting
Online Access:http://hdl.handle.net/10835/14824

Internet

http://hdl.handle.net/10835/14824

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