tion function given a set of heterogeneous,
noisy observations or samples. This algorithm reconstructs the
error-deconvolved or 'underlying' distribution function 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 