期刊
NATIONAL SCIENCE REVIEW
卷 4, 期 4, 页码 627-651出版社
OXFORD UNIV PRESS
DOI: 10.1093/nsr/nwx044
关键词
Big Bayesian Learning; Bayesian non-parametrics; regularized Bayesian inference; scalable algorithms
资金
- National Basic Research Program of China [2013CB329403]
- National Natural Science Foundation of China [61620106010, 61621136008, 61332007]
- Youth Top-notch Talent Support Program
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 learning algorithms, systems and applications with Big Data. Bayesian methods represent one important class of statistical methods for machine learning, with substantial recent developments on adaptive, flexible and scalable Bayesian learning. This article provides a survey of the recent advances in Big learning with Bayesian methods, termed Big Bayesian Learning, 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.
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