I'm having trouble understanding Expectation Maximization specifically in the context of using it fill in missing data. I understand the general principle outlined in previous posts, but I do not see how this is implemented step-wise for missing data.
As an example, if I have sets of repeated data in the format of (a,b,c,d) but I'm missing a single value in one of the series, what are the first pass "E" and "M" steps that lead to predicting the missing value? I assume EM leverages the completed data examples to predict the missing value, but I'm unsure of the steps.
Example 1 - (0,1,1,1)
Example 2 - (1,1,0,0)
Example 3 - (1,0,0,0)
Example 4 - (1,0,1,1)
Example 5 - (1,?,0,1) Missing Data