cmm_fit_em: Estimate Cauchy Mixture parameters using Expectation...

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

Estimate Cauchy Mixture parameters using Expectation Maximisation.

Description

Estimates parameters for Caucy mixture using Expectation Maximisation algorithm.

Usage

    cmm_fit_em( x, p, epsilon = c( 0.000001, 0.000001, 0.000001 ),
                iter.cauchy = 20, 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, gamma1, gamma2, ..., gamman ), where Ai is the proportion of i-th component, mui is the center of i-th component and gammai is the Cauchy scale of i-th component.
epsilon	tolerance threshold for convergence. Structure of epsilon is epsilon = c( epsilon_A, epsilon_mu, epsilon_gamma ), where epsilon_A is threshold for component proportions, epsilon_mu is threshold for component centers and epsilon_gamma is threshold for component Cauchy scales.
iter.cauchy	number of iterations to fit a single Cauchy component.
debug	flag to turn the debug prints on/off.
implementation	flag to switch between C (default) and R implementations.

Value

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

Author(s)

Andrius Merkys

References

Ferenc Nahy. Parameter Estimation of the Cauchy Distribution in Information Theory Approach (2006). Journal of Universal Computer Science

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