Variational Inference: A Review for Statisticians

@article{Blei2016VariationalIA,
  title={Variational Inference: A Review for Statisticians},
  author={David M. Blei and Alp Kucukelbir and Jon D. McAuliffe},
  journal={Journal of the American Statistical Association},
  year={2016},
  volume={112},
  pages={859 - 877}
}
ABSTRACT One of the core problems of modern statistics is to approximate difficult-to-compute probability densities. This problem is especially important in Bayesian statistics, which frames all inference about unknown quantities as a calculation involving the posterior density. In this article, we review variational inference (VI), a method from machine learning that approximates probability densities through optimization. VI has been used in many applications and tends to be faster than… 

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