Big Learning with Bayesian methods

The explosive growth in data volume and the availability of cheap computing resources have sparked increasing interest in Big learning, an emerging subfield that studies scalable machine leaming algorithms, systems and applications with Big Data. Bayesian methods represent one important class of sta...

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Veröffentlicht in:National science review Jg. 4; H. 4; S. 627 - 651
Hauptverfasser: Zhu, Jun, Chen, Jianfei, Hu, Wenbo, Zhang, Bo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford University Press 01.07.2017
Schlagworte:
scalable algorithms
regularized Bayesian inference
Big Bayesian Learning
Bayesian non-parametrics
ISSN:2095-5138, 2053-714X
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Zusammenfassung:The explosive growth in data volume and the availability of cheap computing resources have sparked increasing interest in Big learning, an emerging subfield that studies scalable machine leaming algorithms, systems and applications with Big Data. Bayesian methods represent one important class of statistical methods for machine leaming, with substantial recent developments on adaptive, flexible and scalable Bayesian learning. This artide provides a survey of the recent advances in Big learning with Bayesian methods, termed Big Bayesian Learning0 including non-parametric Bayesian methods for adaptively inferring model complexity, regularized Bayesian inference for improving the flexibility via posterior regularization, and scalable algorithms and systems based on stochastic subsampling and distributed computing for dealing with large-scale applications. We also provide various new perspectives on the large-scale Bayesian modeling and inference.
Bibliographie:Big Bayesian Learning, Bayesian non-parametrics, regularized Bayesian inference, scalablealgorithms
The explosive growth in data volume and the availability of cheap computing resources have sparked increasing interest in Big learning, an emerging subfield that studies scalable machine leaming algorithms, systems and applications with Big Data. Bayesian methods represent one important class of statistical methods for machine leaming, with substantial recent developments on adaptive, flexible and scalable Bayesian learning. This artide provides a survey of the recent advances in Big learning with Bayesian methods, termed Big Bayesian Learning0 including non-parametric Bayesian methods for adaptively inferring model complexity, regularized Bayesian inference for improving the flexibility via posterior regularization, and scalable algorithms and systems based on stochastic subsampling and distributed computing for dealing with large-scale applications. We also provide various new perspectives on the large-scale Bayesian modeling and inference.
10-1088/N
ISSN:2095-5138
2053-714X
DOI:10.1093/nsr/nwx044