Normal Mixture Model

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.
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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.
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Counter-Example(s):
- A Single Normal Distribution Model, which assumes all data is from a single, normally distributed process.
- A Non-Normal Mixture Model, such as a mixture of Poisson or exponential distributions, which does not assume normality of the components.
See: Mixture Model, Gaussian Distribution, Expectation-Maximization Algorithm.

References