We have demonstrated a variant of EM called CEM which optimizes conditional likelihood efficiently and monotonically. The application of CEM and bound maximization to a mixture of Gaussians exhibited promising results and better regression than EM. In other work, a MAP framework with various priors and a deterministic annealing approach have been formulated. Applications of the CEM algorithm to non-linear regressor experts and hidden Markov models are currently being investigated. Nevertheless, many applications CEM remain to be explored and hopefully others will be motivated to extend the initial results.