Maximization Step

The Maximization Step, or M-step, is part of an iterative process called the Expectation-Maximization (EM) algorithm used in statistical modeling. In this step, given current estimates of hidden or unknown data, the algorithm adjusts the model parameters to maximize the likelihood of observing the given data. Essentially, it refines the model by updating parameters so that the model better fits the data based on the current expectations. This process repeats with the Expectation Step until the model parameters stabilize, resulting in the most accurate model possible given the data.