This chapter covers the expectation maximization algorithm and its variants, which are used for joint state and parameter estimation. The presented algorithms include expectation maximization, particle expectation maximization, expectation maximization for Gaussian mixture models, neural expectation maximization, relational neural expectation maximization, variational filtering expectation maximization, and amortized variational filtering expectation maximization. The reviewed applications include stochastic volatility, physical reasoning, and modeling of speech, music, and video.


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