def plot(self, hist=False, show=False, **kwargs):
import matplotlib.pyplot as plt<EOL>if hist:<EOL><INDENT>vals = self._mcpts<EOL>low = vals.min()<EOL>high = vals.max()<EOL>h = plt.hist(<EOL>vals,<EOL>bins=int(np.sqrt(len(vals)) + <NUM_LIT:0.5>),<EOL>histtype="<STR_LIT>",<EOL>normed=True,<EOL>**kwargs<EOL>)<EOL>plt.ylim(<NUM_LIT:0>, <NUM_LIT> * h[<NUM_LIT:0>].max())<EOL><DEDENT>else:<EOL><INDENT>bound = <NUM_LIT><EOL>low = self.rv.ppf(bound)<EOL>high = self.rv.ppf(<NUM_LIT:1> - bound)<EOL>if hasattr(self.rv.dist, "<STR_LIT>"):<EOL><INDENT>low = int(low)<EOL>high = int(high)<EOL>vals = list(range(low, high + <NUM_LIT:1>))<EOL>plt.plot(vals, self.rv.pmf(vals), "<STR_LIT:o>", **kwargs)<EOL><DEDENT>else:<EOL><INDENT>vals = np.linspace(low, high, <NUM_LIT>)<EOL>plt.plot(vals, self.rv.pdf(vals), **kwargs)<EOL><DEDENT><DEDENT>plt.xlim(low - (high - low) * <NUM_LIT:0.1>, high + (high - low) * <NUM_LIT:0.1>)<EOL>if show:<EOL><INDENT>self.show()<EOL><DEDENT>
Plot the distribution of the UncertainVariable. Continuous 
distributions are plotted with a line plot and discrete distributions
are plotted with discrete circles.

Optional
--------
hist : bool
    If true, a histogram is displayed
show : bool
    If ``True``, the figure will be displayed after plotting the 
    distribution. If ``False``, an explicit call to ``plt.show()`` is
    required to display the figure.
kwargs : any valid matplotlib.pyplot.plot kwarg