Another Bias Correction Method John Ashburner

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 kk=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:

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