Summary
This document is a technical report on finite mixture models (FMMs), which are statistical tools used to classify observations and model unobserved heterogeneity. FMMs operate under the premise that observed data belong to unobserved subpopulations, referred to as classes, and utilize mixtures of probability densities or regression models to analyze outcomes. The report outlines the theoretical foundations of FMMs, detailing their application in various fields, such as clustering and modeling phenomena like internet traffic and medical care demand. It describes how FMMs can estimate class membership probabilities and provides a worked example involving a mixture of normal distributions to illustrate the modeling process. The report also discusses the relationship between FMMs and latent class analysis, highlighting their differences and applications in statistical modeling. Additionally, it presents the methodology for fitting FMMs and interpreting the results, including the estimation of class probabilities and the modeling of multimodal data distributions.
