smm_fit_em_GNL08: Estimate Student's t Mixture parameters using Expectation...

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

Estimate Student's t Mixture parameters using Expectation Maximisation.

Description

Estimates parameters for univariate Student's t mixture using Expectation Maximisation algorithm, according to Eqns. 12–17 of Gerogiannis et al. (2009).

Usage

    smm_fit_em_GNL08( x, p, epsilon = c( 1e-6, 1e-6, 1e-6, 1e-6 ),
                      collect.history = FALSE, debug = FALSE,
                      min.sigma = 1e-256, min.ni = 1e-256,
                      max.df = 1000, max.steps = Inf,
                      polyroot.solution = 'jenkins_taub',
                      convergence = abs_convergence,
                      unif.component = FALSE )

Arguments

x	data vector
p	initialisation vector of 4*n parameters, where n is number of mixture components. Structure of p vector is p = c( A1, A2, ..., An, mu1, mu2, ..., mun, k1, k2, ..., kn, ni1, ni2, ..., nin ), where Ai is the proportion of i-th component, mui is the center of i-th component, ki is the concentration of i-th component and nii is the degrees of freedom of i-th component.
epsilon	tolerance threshold for convergence. Structure of epsilon is epsilon = c( epsilon_A, epsilon_mu, epsilon_k, epsilon_ni ), where epsilon_A is threshold for component proportions, epsilon_mu is threshold for component centers, epsilon_k is threshold for component concentrations and epsilon_ni is threshold for component degrees of freedom.
debug	flag to turn the debug prints on/off.

Value

A list.

Author(s)

Andrius Merkys

References

Gerogiannis, D.; Nikou, C. & Likas, A. The mixtures of Student's t-distributions as a robust framework for rigid registration. Image and Vision Computing, Elsevier BV, 2009, 27, 1285–1294 http://www.cs.uoi.gr/~arly/papers/imavis09.pdf

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