While the greedy algorithm is not used very much in practice, it is important know how it functions and mainly its advantages and disadvantages compared to EM and Gibbs sampling. The Greedy algorithm works just like Gibbs sampling except for a main difference in Step 4. Instead of randomly choosing selecting a new starting location, it always picks the starting location with the highest probability.
This makes the Greedy algorithm slightly faster than Gibbs sampling but reduces its chances of finding a global maximum considerably. In cases where the starting location probability distribution is fairly evenly distributed, the greedy algorithm ignores the weights of every other starting position other than the most likely.