Finite Mixture Model

A Finite Mixture Model is a mixture model that define a set of finite mixture function (with a finite number mixture components).

• AKA: Finite Mixture Statistical Model Family.
• Context:
  • It can range from (typically) being a Finite Mixture Density Model (for a mixture density function) to being a Finite Mixture Distribution Model (for a mixture mass function).
  • It can range from being a Two-Component Finite Mixture Model to a Many-Component Finite Mixture Model.
  • It can be an task input to a Finite Mixture Modeling Task.
  • It can be useful for:
    • estimating multimodal or heavy-tailed densities
    • fitting zero-inflated or hurdle models to count data with excess zeros
    • modeling overdispersed data
    • fitting regression models with complex error distributions
    • classifying observations based on predicted component probabilities
    • accounting for unobservable omitted variables
    • estimating switching regressions
• See: Infinite Mixture Model, Latent Factor Models.

---

---

References

1997

• (Friedman & Russell, 1997) ⇒ Nir Friedman, and Stuart J. Russell. (1997). “Image Segmentation in Video Sequences: A Probabilistic Approach.” In: Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence.

1994

• (Bailey & Elkan, 1994) ⇒ Timothy L Bailey, and Charles Elkan. (1994). “Fitting a Mixture Model by Expectation Maximization to Discover Motifs in Bipolymers." In: Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology (ISMB 1994).

---