model, where the error convolution is different for
each data point. Optimization is performed by an Expectation
Maximization (EM) algorithm, extended by a regularization technique
and 'split-and-merge' procedure. These extensions mitigate problems
with singularities and local maxima, which are often encountered
when using the EM algorithm to estimate Gaussian density mixtures."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl