FMM stands for Finite Mixture Model. It is a statistical modeling technique used to represent and analyze data that arises from a mixture of different subpopulations or groups. In a finite mixture model, observations are assumed to be generated from a combination of underlying probability distributions, each representing a distinct subpopulation or component within the overall data.
In this model, each subpopulation is represented by a probability distribution, typically a parametric distribution such as Gaussian or Poisson. The parameters of these distributions, including mean, variance, and shape, may differ across the subpopulations. The mixture proportions, denoting the relative sizes or weights of the subpopulations, are also estimated from the data.
The main objective of using FMM is to identify and estimate the number of subpopulations present in the data, as well as the parameters associated with each subpopulation. By doing so, FMM allows for modeling and capturing the heterogeneity or variation present in the data, which might not be adequately addressed by a single distribution or a traditional statistical model.
FMM finds applications in various fields, including finance, medicine, biology, and social sciences. It is often used for clustering, classification, pattern recognition, and anomaly detection tasks. Estimation of FMM parameters is typically done using numerical techniques such as maximum likelihood or Bayesian methods.
Overall, FMM provides a flexible framework for modeling complex data structures involving multiple subpopulations, enabling a deeper understanding of the underlying processes and patterns.
