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\name{gmm_fit_em}
\alias{gmm_fit_em}
\docType{methods}
\title{Estimate Gaussian Mixture parameters using Expectation Maximization.}
\description{
Estimates parameters for Gaussian mixture using Expectation Maximization algorithm.
}
\usage{
gmm_fit_em( x, p, w = numeric(), epsilon = c( 0.000001, 0.000001, 0.000001 ),
debug = FALSE, implementation = "C", ... )
}
\arguments{
\item{x}{data vector}
\item{p}{
initialization vector of 3*\emph{n} parameters, where \emph{n} is
number of mixture components. Structure of p vector is
p = c( A1, A2, ..., A\emph{n}, mu1, mu2, ..., mu\emph{n}, sigma1, sigma2, ..., sigma\emph{n} ),
where A\emph{i} is the proportion of \emph{i}-th component,
mu\emph{i} is the center of \emph{i}-th component and
sigma\emph{i} is the scale of \emph{i}-th component.
}
\item{w}{
weights of data points, must have the same length as the data
vector; if not given or has different length, equal weights are
assumed.
}
\item{epsilon}{
tolerance threshold for convergence. Structure of epsilon is
epsilon = c( epsilon_A, epsilon_mu, epsilon_sigma ), where
epsilon_A is threshold for component proportions,
epsilon_mu is threshold for component centers and
epsilon_sigma is threshold for component scales.
}
\item{debug}{
flag to turn the debug prints on/off.
}
\item{implementation}{
flag to switch between C (default) and R implementations.
}
\item{...}{
additional arguments passed to gmm_fit_em_R() when R implementation
is used.
}
}
\value{
Vector of mixture parameters, whose structure is the same as of input
parameter's p.
}
\author{Andrius Merkys}
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