why the value of m steps is small why does the m step of expectation maximization algorithm make smaller how do you calculate log-likelihood why is the log likelihood based estimate smaller why do the values in the log likelihood formula get smaller than expected what makes a probability matrix smaller why does the m step of expectation maximization get smaller why is the value of the log likelihood of a function less what makes an algorithm smaller than the first step of an iteration why does the value of m step be smaller than it's value? why does log likelihood function increase after one iteration why m is smaller than 8000 why does the log likelihood function increase how to determine if the value of a log likelihood function is bigger than the value of the prior what is the reason that the m step is smaller in the em algorithm? which algorithm produces smaller log likelihood what makes average likelihood of a function smaller? which of the following is an example of a log likelihood algorithm? what is the problem with the log likelihood algorithm why would my m step be smaller than expected