Variational Inference: A Review for Statisticians

🤖 Plain-English Summary

One of the core problems of modern statistics is to approximate difficult-to-compute probability densities. Our hope in writing this article is to catalyze statistical research on this class of algorithms.

🔑 Key Findings

• 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 classical methods, such as Markov chain Monte Carlo sampling.

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