For ten points, name this hidden variable coordinate ascent algorithm that averages over complete data before parameter estimation for the largest log likelihood.
This algorithm can be interpretated as a sequence of projections between manifolds obtained by alternately minimizing the Kullback-Leibler Divergence between the empirical and complete model distributions.  Maximizing the auxiliary function with respect to the averaging distribution forms the step 1, while maximizing with respect to the parameters in step 2 is equivalent to maximizing the expected complete log likelihood. For ten points, name this hidden variable coordinate ascent algorithm that averages over complete data before parameter estimation for the largest log likelihood.
This algorithm can be interpretated as a sequence of projections between manifolds obtained by alternately minimizing the Kullback-Leibler Divergence between the empirical and complete model distributions.  
EM algorithm; or expectation-maximization algorithm