Inference and Learning from Data Inference

The expectation-maximization (EM) and Baum–Welch algorithms are particularly useful for the processing of data arising from mixture models. Both techniques enable us to identify the parameters of the underlying components, for both cases when the observations are independent of each other or follow a first-order Markovian process. In this chapter, we consider another important example of a mixture model consisting of a collection of independent sources, a mixture matrix, and the observations. The objective is to undo the mixing and recover the original sources. The resulting technique is known as independent component analysis (ICA).