EM for a mixture of k one-dimensional Gaussians. This procedure tends to produce NaNs whenever more Gaussians are being selected than are called for. This is rather convenient. ;-)
TODO cite paper
Given a set of
Data and a number
k of Gaussian peaks, try to find the
optimal GMM. This is done by trying each data point as mu for each Gaussian.
Note that this will be rather slow for larger
k (larger than, say 2 or 3).
In that case, a random-drawing method should be chosen.
TODO xs' -> xs sorting makes me cry!