to the interval between `lb` and `ub`. The underlying
probability density function is normalized accordingly.

Parameters
----------
X : `ContinuousDistribution`
    The random variable to be truncated.
lb, ub : float array-like
    The lower and upper truncation points, respectively. Must be
    broadcastable with one another and the shape of `X`.

Returns
-------
X : `ContinuousDistribution`
    The truncated random variable.

References
----------
.. [1] "Truncated Distribution". *Wikipedia*.
       https://en.wikipedia.org/wiki/Truncated_distribution

Examples
--------
Compare against `scipy.stats.truncnorm`, which truncates a standard normal,
*then* shifts and scales it.

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from scipy import stats
>>> loc, scale, lb, ub = 1, 2, -2, 2
>>> X = stats.truncnorm(lb, ub, loc, scale)
>>> Y = scale * stats.truncate(stats.Normal(), lb, ub) + loc
>>> x = np.linspace(-3, 5, 300)
>>> plt.plot(x, X.pdf(x), '-', label='X')
>>> plt.plot(x, Y.pdf(x), '--', label='Y')
>>> plt.xlabel('x')
>>> plt.ylabel('PDF')
>>> plt.title('Truncated, then Shifted/Scaled Normal')
>>> plt.legend()
>>> plt.show()

However, suppose we wish to shift and scale a normal random variable,
then truncate its support to given values. This is straightforward with
`truncate`.

>>> Z = stats.truncate(scale * stats.Normal() + loc, lb, ub)
>>> Z.plot()
>>> plt.show()

Furthermore, `truncate` can be applied to any random variable:

>>> Rayleigh = stats.make_distribution(stats.rayleigh)
>>> W = stats.truncate(Rayleigh(), lb=0, ub=3)
>>> W.plot()
>>> plt.show()

r