EM-algorithm and missing data

Question

Does EM-algorithm only work with missing data? If not, what is the idea to assume that we have missing data?

Any density that can write as$$f_\theta(x)=\int_\mathscr{Y} g_\theta(x,y)\text{d}y$$qualifies for EM, not truly a restriction. Provided you can implement the E step. — Xi'an, Sep 21 '16 at 08:31
Check also: http://stats.stackexchange.com/questions/72774/numerical-example-to-understand-expectation-maximization/72810#72810 — Tim, Sep 21 '16 at 08:42

score 5 · Accepted Answer · edited Sep 21 '16 at 08:41

5

I think the Wikipedia for EM algorithm has everything you want and it probably explains better than I do. You can also use the EM algorithm for latent variables; no missing data but variables that you can't observe and measure. The idea of the algorithm is to iteratively adjust your missing data/latent variables until your maximum likelihood estimate converges.

edited Sep 21 '16 at 08:41

Tim

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answered Sep 21 '16 at 08:03

SmallChess

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EM-algorithm and missing data

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