Estimating parameters and the mixture component number of a GMM in the presence of unoserved data

The Gaussian Mixture Model (GMM) is one of powerful approaches to model data that is heterogeneous and stems from multiple populations. However, in some certain situations, a part of dataset is unobservable owing to censoring problem. This problem refers to the fact that the value of a measurement or observation is only partially known.