variational inference

(55 minutes to learn)

Summary

In most probabilistic models of interest, it's intractable to compute posterior marginals and/or normalizing constants exactly. Variational inference is a framework for approximating both. Variational inference treats inference as an optimization problem: we're trying to find a distribution (or a representation resembling a distribution) which is as close as possible to the true posterior, according to some measure.

Context

This concept has the prerequisites:

Core resources (read/watch one of the following)

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Supplemental resources (the following are optional, but you may find them useful)

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See also