Mixture models is a statistical modeling approach used to represent the probability distribution of an observed variable as a convex combination of simpler component distributions, where the component membership for each observation is unknown and treated as a latent variable. This methodology is significant for analyzing data exhibiting heterogeneity, enabling tasks such as density estimation, clustering, and inferring underlying subpopulations within a dataset.