Expectation Maximization Algorithm

The Expectation Maximization algorithm is an iterative method used to find maximum likelihood estimates of parameters in models that depend on unobserved latent variables. It alternates between estimating the hidden states given the current parameters and updating the parameters to maximize the likelihood of the observed data.

This algorithm is critical for training Hidden Markov Models where the true regime is never directly observed. It allows for the systematic refinement of models as more data becomes available.

By iteratively improving the model fit, it helps traders capture subtle changes in market behavior. It is a powerful tool for handling incomplete data in complex financial environments.