computes an estimator and a deterministic upper bound of the probability Pr(l<X<u), where X is a zero-mean multivariate normal vector with covariance matrix Σ, that is, X is drawn from N(0,Σ) infinite values for vectors u and l are accepted;
computes with tail-precision the quantile function of the standard normal distribution at 0≤ p≤ 1, and truncated to the interval [l,u]; Infinite values for vectors l and u are accepted;
simulates nidentically and independently distributed random vectors from the d-dimensional N(0,Σ) distribution (zero-mean normal with covariance Σ) conditional on l<X<u; infinite values for l and u are accepted;
efficient state-of-the-art generator of a vector of length(l)=length(u) from the standard multivariate normal distribution, truncated over the region [l,u]; infinite values for u and l are accepted;
computes an estimator and a deterministic upper bound of the probability Pr(l<X<u), where X is a zero-mean multivariate normal vector with covariance matrix Σ, that is, X is drawn from N(0,Σ) infinite values for vectors u and l are accepted; Monte Carlo method uses sample size n;
providing 
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