smm_fit_em_APK10: 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 Fig. 2 of Aeschliman et al. (2010).

Usage

    smm_fit_em_APK10( x, p, epsilon = c( 1e-6, 1e-6, 1e-6, 1e-6 ),
                      collect.history = FALSE, debug = 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.
collect.history	flag to turn accumulation of estimation history on/off.
debug	flag to turn the debug prints on/off.

Value

A list.

Author(s)

Andrius Merkys

References

Aeschliman, C.; Park, J. & Kak, A. C. A Novel Parameter Estimation Algorithm for the Multivariate t-Distribution and Its Application to Computer Vision European Conference on Computer Vision 2010, 2010 https://engineering.purdue.edu/RVL/Publications/Aeschliman2010ANovel.pdf

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