EM algorithms for multivariate Gaussian mixture models with truncated and censored data

We present expectation–maximization (EM) algorithms for fitting multivariate Gaussian mixture models to data that are truncated, censored or truncated and censored. These two types of incomplete measurements are naturally handled together through their relation to the multivariate truncated Gaussian distribution. We illustrate our algorithms on synthetic and flow cytometry data.