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# scipy.maxentropy.conditionalmodel.pmf_function

`conditionalmodel.``pmf_function`(f=None)

  Returns the pmf p_theta(x) as a function taking values on the model’s sample space. The returned pmf is defined as:

  > p_theta(x) = exp(theta.f(x) - log Z)

  where theta is the current parameter vector self.params. The returned function p_theta also satisfies

  > all([p(x) for x in self.samplespace] == pmf()).

  The feature statistic f should be a list of functions [f1(),...,fn(x)]. This must be passed unless the model already contains an equivalent attribute ‘model.f’.

  Requires that the sample space be discrete and finite, and stored as self.samplespace as a list or array.

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