Inheritance diagram for nipy.neurospin.clustering.GGMixture:
One-dimensional Gamma-Gaussian mixture density classes : Given a set of points the algo provides approcumate maximum likelihood estimates of the mixture distribution using an EM algorithm
Author: Bertrand Thirion and Merlin Keller 2005-2008
This is the basic one dimensional Gamma-Gaussian-Gamma Mixture estimation class, where the frist gamma has a negative sign, while the second one has a positive sign 7 parameters are used: - shape_n: negative gamma shape - scale_n: negative gamma scale - mean: gaussian mean - var: gaussian variance - shape_p: positive gamma shape - scale_p: positive gamma scale - mixt: array of mixture parameter (weights of the n-gamma,gaussian and p-gamma)
ROC curve for seperating positive Gamma distribution from two other modes, predicted by current parameter values -x: vector of observations
Output: P P[0]: False positive rates P[1]: True positive rates
This is the basic one dimensional Gaussian-Gamma Mixture estimation class Note that it can work with positive or negative values, as long as there is at least one positive value. NB : The gamma distribution is defined only on positive values. 5 parameters are used: - mean: gaussian mean - var: gaussian variance - shape: gamma shape - scale: gamma scale - mixt: mixture parameter (weight of the gamma)
This is the basic one dimensional Gaussian-Gamma Mixture estimation class Note that it can work with positive or negative values, as long as there is at least one positive value. NB : The gamma distribution is defined only on positive values. 5 parameters are used: - mean: gaussian mean - var: gaussian variance - shape: gamma shape - scale: gamma scale - mixt: mixture parameter (weight of the gamma)