variational interpretation of EM

(50 minutes to learn)


The expectation-maximization (EM) algorithm can be interpreted as a coordinate ascent procedure which optimizes a variational lower bound on the likelihood function. This connects it with variational inference algorithms and justifies various generalizations and approximations to the algorithm.


This concept has the prerequisites:

Core resources (read/watch one of the following)


See also

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