A rejection technique for sampling from T-concave distributions
ACM Transactions on Mathematical Software (TOMS)
Automatic sampling with the ratio-of-uniforms method
ACM Transactions on Mathematical Software (TOMS)
Algorithm 802: an automatic generator for bivariate log-concave distributions
ACM Transactions on Mathematical Software (TOMS)
A hybrid Markov chain for the Bayesian analysis of the multinomial probit model
Statistics and Computing
Perfect simulation of positive Gaussian distributions
Statistics and Computing
Gaussian Markov Random Fields: Theory And Applications (Monographs on Statistics and Applied Probability)
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Semiparametric regression with shape-constrained penalized splines
Computational Statistics & Data Analysis
Bayesian Learning of Noisy Markov Decision Processes
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special Issue on Monte Carlo Methods in Statistics
Sequential Monte Carlo EM for multivariate probit models
Computational Statistics & Data Analysis
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We consider the problem of simulating a Gaussian vector X, conditional on the fact that each component of X belongs to a finite interval [a i ,b i ], or a semi-finite interval [a i ,+驴). 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.