gmm_fit_em: Estimate Gaussian Mixture parameters using Expectation...

In merkys/MixtureFitting: Fitting of univariate mixture distributions to data using various approaches

Estimate Gaussian Mixture parameters using Expectation Maximisation.

Description

Estimates parameters for Gaussian mixture using Expectation Maximisation algorithm.

Usage

    gmm_fit_em( x, p, epsilon = c( 0.000001, 0.000001, 0.000001 ),
                debug = FALSE, implementation = "C", ... )

Arguments

x	data vector
p	initialisation vector of 3*n parameters, where n is number of mixture components. Structure of p vector is p = c( A1, A2, ..., An, mu1, mu2, ..., mun, sigma1, sigma2, ..., sigman ), where Ai is the proportion of i-th component, mui is the center of i-th component and sigmai is the scale of i-th component.
epsilon	tolerance threshold for convergence. Structure of epsilon is epsilon = c( epsilon_A, epsilon_mu, epsilon_sigma ), where epsilon_A is threshold for component proportions, epsilon_mu is threshold for component centers and epsilon_sigma is threshold for component scales.
debug	flag to turn the debug prints on/off.
implementation	flag to switch between C (default) and R implementations.
...	additional arguments passed to gmm_fit_em_R() when R implementation is used.

Value

Vector of mixture parameters, whose structure is the same as of input parameter's p.

Author(s)

Andrius Merkys

merkys/MixtureFitting documentation built on May 25, 2024, 9:01 a.m.