What are good criteria for deciding when to terminate the expectation-maximization algorithm.

I know that the idea is that you should terminate when the change in the data log likelihood is "small" or the change in the model parameters is "small" over one (or a few) iterations. But how do you determine "small"?

So far, my literature search has only provided papers that prove convergence under different conditions, or provide specialized techniques to improve the rate of convergence. I've not seen good, practical, results on when to terminate the algorithm in practical use.

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