GMMlle
GMMlle is a fictional probabilistic modeling framework described in speculative discussions of machine learning. It is presented as an extension of Gaussian Mixture Models that incorporates a dynamic latent state to capture how cluster assignments evolve over time or across changing conditions. In these contexts, GMMlle is used to illustrate how probabilistic clustering can accommodate shifts in data-generating processes while retaining the interpretability of Gaussian components.
Model overview: GMMlle retains the mixture-of-Gaussians structure, with a finite set of Gaussian components each defined
Relationship and use: In theory, GMMlle provides a bridge between static clustering and time-aware sequence modeling,
See also: Gaussian mixture model, hidden Markov model, latent-variable model, EM algorithm, variational inference.