In this article, we propose an efficient approach for inverting computationally expensive
cumulative distribution functions. A collocation method, called the Stochastic Collocation
Monte Carlo sampler (SCMC sampler), within a polynomial chaos expansion framework,
allows us the generation of any number of Monte Carlo samples based on only a few
inversions of the original distribution plus independent samples from a standard normal
variable. We will show that with this path-independent collocation approach the exact …