def MakePmf(self, steps=<NUM_LIT>, name='<STR_LIT>'):
if self.alpha < <NUM_LIT:1> or self.beta < <NUM_LIT:1>:<EOL><INDENT>cdf = self.MakeCdf()<EOL>pmf = cdf.MakePmf()<EOL>return pmf<EOL><DEDENT>xs = [i / (steps - <NUM_LIT:1.0>) for i in xrange(steps)]<EOL>probs = [self.EvalPdf(x) for x in xs]<EOL>pmf = MakePmfFromDict(dict(zip(xs, probs)), name)<EOL>return pmf<EOL>
Returns a Pmf of this distribution.

        Note: Normally, we just evaluate the PDF at a sequence
        of points and treat the probability density as a probability
        mass.

        But if alpha or beta is less than one, we have to be
        more careful because the PDF goes to infinity at x=0
        and x=1.  In that case we evaluate the CDF and compute
        differences.