class_docstr: mixtures with a discrete distribution
  
  The mixing distribution is a discrete distribution like scipy.stats.poisson.
  All distribution in the mixture of the same type and parametrized
  by the outcome of the mixing distribution and have to be a continuous
  distribution (or have a pdf method).
  As an example, a mixture of normal distributed random variables with
  Poisson as the mixing distribution.
  
  
  assumes vectorized shape, loc and scale as in scipy.stats.distributions
  
  assume mixing_dist is frozen
  
  initialization looks fragile for all possible cases of lower and upper
  bounds of the distributions.
code: def cdf(self, x):
      x = np.asarray(x)
      if np.size(x) > 1:
          x = x[..., None]  # [None, ...]
      bd_probs = self.base_dist.cdf(x, *self.bd_args, **self.bd_kwds)
      prob = (bd_probs * self.mixing_probs).sum(-1)
      return prob, bd_probs
  
file_path: statsmodels/statsmodels/sandbox/distributions/otherdist.py
imports: import numpy
  from scipy import stats
label_desc: Use image node_id 4 for calling the ParametricMixtureD obj's underlying member method code with example usage: obj.cdf(x) and returns: prob,  bd_probs