AmazaspShumik/Mixture-Models

This project is a historical reference implementation of classical machine learning algorithms (HME and MDN) dating back over a decade. With only 45 stars in nearly 11 years and zero current velocity, it serves primarily as an educational archive rather than a functional piece of modern infrastructure. The defensibility is near zero as these algorithms are now standard components in well-maintained libraries like Scikit-learn (for GMMs/EM), TensorFlow Probability, or Pyro (for Bayesian/MDN implementations). While Mixture of Experts (MoE) is currently a 'frontier' topic in LLM architecture (e.g., GPT-4, Mixtral), this repository implements the 1994-era Jordan/Jacobs HME approach, which does not utilize modern deep learning frameworks (PyTorch/JAX) or hardware acceleration. It has been effectively displaced by the evolution of the deep learning ecosystem and the integration of these probabilistic methods into broader, production-ready frameworks.