Normal Mixture Model

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A Normal Mixture Model is a mixture model that represents a distribution as a mixture of several normal (Gaussian) distributions.

  • Context:
    • It can (typically) be used to Process Modeling for processes believed to be generated from several distinct but overlapping processes, each with a normal distribution.
    • It can (typically) involve estimating parameters such as the means, variances, and mixing proportions of the component normal distributions.
    • It can (often) be applied in fields like machine learning, finance, and biology, where data may exhibit multiple modes or clusters.
    • It can be a flexible approach for modeling complex distributions that cannot be adequately described by a single normal distribution.
    • It can employ parameter estimation algorithms like the Expectation-Maximization (EM) algorithm.
    • ...
  • Example(s):
    • a Zero-Mean Normal Mixture Model.
    • A normal mixture model used in finance to model the distribution of asset returns, which may exhibit multiple peaks corresponding to different market conditions.
    • In biology, to model the distribution of a trait that is influenced by multiple genetic and environmental factors.
    • ...
  • Counter-Example(s):
  • See: Mixture Model, Gaussian Distribution, Expectation-Maximization Algorithm.


References

2024

  • (Not Yet Available)