ssd_gradient: Sum of Squared Differences Using Gaussian Mixture...

In merkys/MixtureFitting: Fitting of Univariate Mixture Distributions to Data using Various Approaches

Sum of Squared Differences Using Gaussian Mixture Distribution

Description

Gradient (derivative) function of ssd().

Usage

    ssd_gradient( x, y, p )

Arguments

x	data vector
y	response vector
p	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, sigma1, sigma2, ..., sigman ), where Ai is the proportion of i-th component, mui is the location of i-th component, sigmai is the scale of i-th component.

Value

Gradient values measured at x.

Author(s)

Andrius Merkys

merkys/MixtureFitting documentation built on July 5, 2025, 5:43 a.m.