cmm_init_vector_kmeans: 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 Maximization.

Description

Estimate an initialization vector for Cauchy mixture fitting using k-means. R implementation of k-means in kmeans() is used to find data point assignment to clusters. Then several iterations of Cauchy mixture fitting (per Nahy 2006) is used to derive mixture parameters.

Usage

    cmm_init_vector_kmeans( x, m, iter.cauchy = 20 )

Arguments

x	data vector
m	number of mixture components
iter.cauchy	number of iterations to fit a single Cauchy component.

Value

Parameter 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.

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 July 5, 2025, 5:43 a.m.