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We develop a computationally efficient and robust algorithm for generatingpseudo-random samples from a broad class of smooth probability distributions inone and two dimensions. The algorithm is based on inverse transform samplingwith a polynomial approximation scheme using Chebyshev polynomials, Chebyshevgrids, and low rank function approximation. Numerical experiments demonstratethat our algorithm outperforms existing approaches.

Fast inverse transform sampling in one and two dimensions