The document discusses using the EM algorithm to estimate parameters in a generalized linear finite mixture model. It introduces the model and describes how the likelihood equation can be split into two terms, allowing the estimators to be separated into an iterative M-step that fits two standard non-mixture problems using weights and multinomial or Poisson data. An example application fits Weibull distributions to mutant and control mouse data.