## Expectation Maximization with an Example

In the last post, we introduced a technique called the Maximum Likelihood Estimation (MLE) to estimate unknown parameters of a probability distribution given a set of observations. Although it is a very useful technique, but it assumes that all information about the observation is available to us. Consider the example of a two coin toss : "Given two coins A and B, with probability of heads being 'p' and 'q' […]