<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def probabilistic_collocation(order, dist, subset=.1):
 """ Probabilistic collocation method. Args: order (int, numpy.ndarray) : Quadrature order along each axis. dist (Dist) : Distribution to generate samples from. subset (float) : Rate of which to removed samples. """

    abscissas, weights = chaospy.quad.collection.golub_welsch(order, dist)

    likelihood = dist.pdf(abscissas)

    alpha = numpy.random.random(len(weights))
    alpha = likelihood > alpha*subset*numpy.max(likelihood)

    abscissas = abscissas.T[alpha].T
    weights = weights[alpha]
    return abscissas, weights