Zero-Mean Normal Mixture Model
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A Zero-Mean Normal Mixture Model is a normal mixture model (that represents a distribution as a mixture) of multiple normal distribution models with a mean of zero.
- Context:
- It can (typically) be used to model data that is generated from multiple underlying processes, where each process produces outcomes centered around zero but with varying variances.
- It can (often) be applied in fields like signal processing, finance, and machine learning where modeling of complex distributions is necessary.
- It can (often) require the estimation of parameters like the variances of the normal distributions and their mixing proportions.
- It can be particularly useful in scenarios where the zero-centered nature of the data is a significant characteristic, such as in certain types of anomaly detection.
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- Example(s):
- A zero-mean normal mixture model used to analyze financial returns, which often exhibit heavy tails and a peak at zero.
- In signal processing, where it might be used to model noise that is composed of several different underlying types.
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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-Zero Mean Normal Mixture Model, where the component normal distributions are allowed to have non-zero means.
- See: Mixture Model, Gaussian Distribution, Model Fitting.