Utilities for fitting mixture models to data extending Distributions.jl. This is a new package; at the moment it implements the Expectation-Maximisation algorithm to fit mixture distributions to data using Maximum Likelihood Estimation (MLE). Suggestions and feedback very welcome.
Expectation-Maximisation is an iterative algorithm to fit mixture distributions to data. A single step of EM can be performed in closed form for many types of distributions including exponential family distributions. Every iteration of EM is guaranteed to increase the likelihood of the model, and a simple implementation of the fitting procedure stops when the likelihood stops changing meaningfully.
fit_em(mixture::AbstractMixtureDistribution, samples::AbstractVector[, weights::AbstractWeights]; tol=1e-4, maxiter=1000, kwargs...)
Fit a mixture to the given samples by iteratively performing EM steps until the total log likelihood improves by less than tol. The remaining kwargs are passed to emstep. Returns the fitted mixture and a list containing the total log likelihood after each iteration.
emstep(mixture::AbstractMixtureDistribution, samples::AbstractVector[, weights::AbstractWeights]; constructor=MixtureModel)
Perform an Expectation-Maximisation step for a mixture distribution. Here constructor is a function that takes a list of components and a weight vector and returns a new mixture.
emstep(comp::Distribution, samples::AbstractVector[, weights::AbstractWeights]; kwargs...)
Perform an Expectation-Maximisation step for a single distribution; in most cases this directly computes the MLE.
This package uses Julia's type dispatch and supports mixture distributions of various types (including mixtures of different distribution families).
GeometricPoissonBernoulliBinomial(for fixedn)NegativeBinomial(following [#1])
BetaExponentialErlang(for fixedα)GammaNormal
- MixFit.jl supports a subset of the above distributions and uses moment matching instead of MLE for each EM step
- MixtureModels.jl (old) is defunct
- GaussianMixtures.jl is specialised for Gaussian Mixture Models
[1] C. Huang, X. Liu, T. Yao and X. Wang, "An efficient EM algorithm for the mixture of negative binomial models", Journal of Physics Conference Series 1324(1), 044104 (2019). https://doi.org/10.1088/1742-6596/1324/1/012093