2010/01/06 by Nicolas Chopin, ChopinNicolas · 1 voice
Computer Science · Decision Sciences · Mathematics · #Algorithm #Applied mathematics #Bounded function #Combinatorics #Component (thermodynamics) #Computer science #Data mining #Discrete mathematics #Extension (predicate logic) #Gaussian #Gaussian Processes and Bayesian Inference #Interval (graph theory) #Interval arithmetic #Markov Chains and Monte Carlo Methods #Mathematical analysis #Mathematical optimization #Mathematics #Simulation Techniques and Applications #Statistics #Table (database) #Truncation (statistics) #acm:65C10 #msc:65C10 #stat.CO
paper · pdf · doi:10.1007/s11222-009-9168-1
published as Statistics and Computing 2011, Volume 21, Number 2, 275-288
openalex publication_date 2010/01/06 · arxiv created 2012/01/30 · arxiv published 2012/01/30 · arxiv updated 2012/01/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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,+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.