Fast simulation of truncated Gaussian distributions

Abstract

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,+infty). 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 our 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 3 or more dimensions is discussed.

References in corpus (1)

• Simulation of truncated normal variables

Cited by in corpus (22)

• The Normal Law Under Linear Restrictions: Simulation and Estimation via Minimax Tilting
• Leave Pima Indians alone: binary regression as a benchmark for Bayesian computation
• Conjugate Bayes for probit regression via unified skew-normal distributions
• A hierarchical Bayesian perspective on majorization-minimization for non-convex sparse regression: application to M/EEG source imaging
• Bayesian anti-sparse coding
• An Efficient Sampling Algorithm for Non-smooth Composite Potentials
• A Class of Conjugate Priors for Multinomial Probit Models which Includes the Multivariate Normal One
• Distributed Adaptive Gradient Algorithm with Gradient Tracking for Stochastic Non-Convex Optimization
• Fast Gibbs sampling for high-dimensional Bayesian inversion
• Sequential Monte Carlo EM for multivariate probit models
• Fast Simulation of Hyperplane-Truncated Multivariate Normal Distributions
• Universal adversarial perturbations for multiple classification tasks with quantum classifiers
• Finite-dimensional approximation of Gaussian processes with inequality constraints
• Estimating Stochastic Production Frontiers: A One-stage Multivariate Semi-Nonparametric Bayesian Concave Regression Method
• Bayesian Nonparametric Unmixing of Hyperspectral Images
• Top-K Deep Video Analytics: A Probabilistic Approach
• Bayesian learning of noisy Markov decision processes
• Mid-price estimation for European corporate bonds: a particle filtering approach
• Trace your sources in large-scale data: one ring to find them all
• Unsupervised Learning with Truncated Gaussian Graphical Models
• Bayesian Nonparametric Feature and Policy Learning for Decision-Making
• Computation of Gaussian orthant probabilities in high dimension