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src/n/i/nipy-0.3.0/nipy/algorithms/statistics/empirical_pvalue.py   nipy(Download)
        gd.set([x.min(), x.max()], 100)
        gdm = gd.make_grid().squeeze()
        lj = BayesianGMM.likelihood(gd.make_grid())
 
    # estimate the prior weights