This poster will present a non-parametric approach, based on optimising an objective function similar to the entropy of the log-transformed intensity distribution, but using histograms of non-transfor
This poster will present a non-parametric approach, based on optimising an objective function similar to the entropy of the log-transformed intensity distribution, but using histograms of non-transformed intensities.
Mixture of Gaussians Based Derivation - I
A distribution can be modelled by a mixture of Gaussians (MOG). For univariate data, the kth Gaussian is modelled by a mean (k), variance (k2) and mixing proportions (k, where kk=1 and k>=0). Fitting a MOG involves maximising the likelihood of the data (y), given the parameterisations. The likelihood of a datum with intensity yi, given that it belongs to the kth Gaussian is:
