The toolbox includes:
1. fast random number generators from the truncated univariate and multivariate student/normal distributions;
2. (Quasi-) Monte Carlo estimator of the cumulative distribution function of the multivariate student/normal;
3. accurate computation of the quantile function of the normal distribution in the extremes of its tails.
Z. I. Botev (2017), The Normal Law Under Linear Restrictions: Simulation and Estimation via Minimax Tilting, Journal of the Royal Statistical Society, Series B, Volume 79, Part 1, pp. 1-24
Zdravko Botev (2021). Truncated Normal and Student's t-distribution toolbox (https://www.mathworks.com/matlabcentral/fileexchange/53796-truncated-normal-and-student-s-t-distribution-toolbox), MATLAB Central File Exchange. Retrieved .
Thanks for your sampling method and it really helped me a lot, but when I tried the scalar case, error always occurred saying that
Inner matrix dimensions must agree.
Error in cholperm (line 20)
Could you please take a look at it?
What can I do to sample from a truncated multivariate Gaussian with non-zero mean when the number of inequality constraints is greater than the dimensionality of the random vector (matrix A is not squared)?
Thanks for sharing. Best regards
Thanks for making this toolbox available!
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