GaussianMixtures
GaussianMixtures.jl is a Julia package for working with Gaussian mixture models.
About GaussianMixtures
GaussianMixtures.jl is a Julia package for Gaussian Mixture Models. It provides training with the Expectation Maximization algorithm, likelihood calculation, model adaptation, and input/output for models.
It covers only multivariate normal distributions, with diagonal covariance, full covariance, and full covariance variational Bayes variants. It supports parallel computation of Baum-Welch statistics and larger-than-memory data through incorporated BigData handling, with conversion to MixtureModels types that requires transposing data due to differing layout conventions.
Key features
- Train GMM parameters using Expectation Maximization algorithm
- Calculate likelihood for Gaussian mixture models
- Adapt existing Gaussian mixture models
- Support diagonal and full covariance GMMs
- Support full covariance variational Bayes GMMs
- Handle larger-than-memory data through incorporated BigData support
Best for: Julia users who need to train and evaluate Gaussian mixture models on multivariate data, including large datasets.
Good for
- Focused on multivariate normal distributions rather than general mixture types
- Supports parallel Baum-Welch computation and larger-than-memory data
- Includes diagonal, full covariance, and variational Bayes options
Watch out for
- Limited to Gaussian components and does not cover other mixture distributions
- Uses opposite data orientation to Distributions.jl, requiring transpose when converting types
GaussianMixtures at a glance
- Category
- Development › Developer Tools
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Not stated
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
Guides for Developer Tools
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Open source
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GaussianMixtures FAQ
How is the package installed?
The homepage shows installation with Pkg.add("GaussianMixtures") in Julia.
Which GMM variants are implemented?
Diagonal covariance GMMs, full covariance GMMs, and full covariance variational Bayes GMMs.
How should training data be arranged?
As an nx by d matrix where data points run down rows; transpose data when converting to MixtureModels from Distributions.jl.
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