Maximum likelihood estimation of K-distribution parameters via the expectation-maximization algorithm

Maximum likelihood (ML) estimates of K-distribution parameters are derived using the expectation maximization (EM) approach. This approach demonstrates the computational advantages compared with 2-D numerical maximization of the likelihood function using a Nelder-Mead approach. For large datasets, the EM approach yields more accurate estimates than those of a non-ML estimation technique.