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we use the data from the expectation step as if it were
actually measured data to get the ML estimate of the parameter
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this is done by taking the derivative of the log-likelihood
function with respect to the parameter p, equating it to zero and
solving for p we get
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substituting (x2)k+1 in the above
equation with pk we get
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for the given problem let us consider a case where actual value
of p = 0.5, n = 100, y1 = 63. Also let us start with
the initial estimate of p = 0
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proceeding with the iteration as shown in the above equations
we get the following results at various steps: