Fast simulation of truncated Gaussian distributions

We consider the problem of simulating a Gaussian vector X, conditional on the fact that each component of X belongs to a finite interval [ai,bi], or a semi-finite interval [ai,+∞). In the one-dimensional case, we design a table-based algorithm that is computationally faster than alternative algorithms. In the two-dimensional case, we design an accept-reject algorithm. According to our calculations and numerical studies, the acceptance rate of this algorithm is bounded from below by 0.5 for semi-finite truncation intervals, and by 0.47 for finite intervals. Extension to three or more dimensions is discussed.

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