gamma-distribution

I'm trying to use the Gamma distribution from boost::math but it looks like it isn't possible to use it with boost::variate_generator. Could someone confirm that? Or is there a way to use it. I discovered that there is a boost::gamma_distribution undocumented that could probably be used too but it only allows to choose the alpha parameter from the distribution and not the beta. Thanks! As mentioned in this link , you can extend Boost's (or TR1's) one-parameter gamma distribution simply by multiplying the output of the rng by your desired scale. Below is sample code that uses variate_generator

I am trying to fit a gamma mixture model (two gamma distributions) to an age-fertility profile. I have a dataset containing age specific fertility rates and age, and I want to fit two gammas in order to find the corresponding parameters (in the end I will use fertility profiles from different years and try to see how the parameters evolve over time). I have so far tried to use mixtools library (gammamixEM) but without success. I would be very grateful for some help. Ale a<- structure(list(EDAD = structure(1:45, .Label = c("11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22",

Color space . Well, everybody knows about RGB: three values normalized in the range [0.0,1.0], which have the meaning of the intensity of the color components Red Green Blue; this intensity is meant as linear, isn't? Gamma . As far I can understand, gamma is a function which maps RGB color components to another value. Googling on this, I've seen linear functions and non linear functions... Linear functions seems to scale RGB components, so it seems to tune image brightness; non linear functions seems to "decompress" darker/lighter components. Now, I'm starting to implement an image viewer,

For a classification task I want to fit a gamma distribution to two pair of data: Distance population within class and between class. This is to determine the theoretical False Accept and False Reject Rate. The fit Scipy returns puzzles me tough. A plot of the data is below, where circles denote within class distances and x-es between class distance, the solid line is the fitted gamma within class, the dotted line is the fitted gamma on the between class distance. What I would have expected is that the gamma curves would peak at around ~10 and ~30, not at 0 for both. Does anyone see what's

Can anyone help me out in fitting a gamma distribution in python? Well, I've got some data : X and Y coordinates, and I want to find the gamma parameters that fit this distribution... In the Scipy doc , it turns out that a fit method actually exists but I don't know how to use it :s.. First, in which format the argument "data" must be, and how can I provide the second argument (the parameters) since that's what I'm looking for? Generate some gamma data: import scipy.stats as stats alpha = 5 loc = 100.5 beta = 22 data = stats.gamma.rvs(alpha, loc=loc, scale=beta, size=10000) print(data) # [ 202