n common to
all samples, even when the individual samples have unique error and
missing-data properties. The underlying distribution is modeled as
a mixture of Gaussians, which is completely general. Model
parameters are chosen to optimize a justified, scalar objective
function: the logarithm of the probability of the data under the
error-convolved model, where the error convolution is different for
each data point. Optimization is performed by an Expectation
Maximization (EM) algorithm, extended by a regul