Variational EM

Variational EM is a statistical method used to estimate complex models, particularly in situations where data is incomplete or hidden. It combines two main steps: the "E" step, where a simpler approximation of the hidden variables is created, and the "M" step, where the model parameters are updated based on this approximation. By iterating between these steps, it aims to find the best fit for the data. This approach is particularly useful in fields like machine learning and data analysis, where managing uncertainty and making sense of large datasets is essential.