QSIMVN

QSIMVN: A Matlab function with supporting functions, for the numerical computation of multivariate normal distribution values. The method used is similar to the method used by the Fortran MVNDST software, but the quasi-random integration point set is different. QSIMVNV is a vectorized version of this software which is usually much faster than QSIMVN. This function uses an algorithm given in the paper ”Numerical Computation of Multivariate Normal Probabilities”, in J. of Computational and Graphical Stat., 1(1992), pp. 141-149, by Alan Genz.


References in zbMATH (referenced in 145 articles )

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  1. Beranger, B.; Stephenson, A. G.; Sisson, S. A.: High-dimensional inference using the extremal skew-(t) process (2021)
  2. Cao, Jian; Genton, Marc G.; Keyes, David E.; Turkiyyah, George M.: Exploiting low-rank covariance structures for computing high-dimensional normal and Student-(t) probabilities (2021)
  3. Gleason, Joseph D.; Vinod, Abraham P.; Oishi, Meeko M. K.: Lagrangian approximations for stochastic reachability of a target tube (2021)
  4. Hintz, Erik; Hofert, Marius; Lemieux, Christiane: Normal variance mixtures: distribution, density and parameter estimation (2021)
  5. Huang, Jingfang; Cao, Jian; Fang, Fuhui; Genton, Marc G.; Keyes, David E.; Turkiyyah, George: An (O(N)) algorithm for computing expectation of (N)-dimensional truncated multi-variate normal distribution. I: Fundamentals (2021)
  6. Rabier, Charles-Elie; Delmas, Céline: The SgenoLasso and its cousins for selective genotyping and extreme sampling: application to association studies and genomic selection (2021)
  7. Teichmann, Jakob; Menzel, Peter; Heinig, Thomas; van den Boogaart, Karl Gerald: Modeling and fitting of three-dimensional mineral microstructures by multinary random fields (2021)
  8. Vinod, Abraham P.; Oishi, Meeko M. K.: Stochastic reachability of a target tube: theory and computation (2021)
  9. Bachoc, François; Helbert, Céline; Picheny, Victor: Gaussian process optimization with failures: classification and convergence proof (2020)
  10. Benavoli, Alessio; Azzimonti, Dario; Piga, Dario: Skew Gaussian processes for classification (2020)
  11. Khaniyev, Taghi; Kayış, Enis; Güllü, Refik: Next-day operating room scheduling with uncertain surgery durations: exact analysis and heuristics (2020)
  12. Wang, Jialei; Clark, Scott C.; Liu, Eric; Frazier, Peter I.: Parallel Bayesian global optimization of expensive functions (2020)
  13. Xie, Jietao; Wu, Juan: Recursive calculation model for a special multivariate normal probability of first-order stationary sequence (2020)
  14. Zhang, Hong; Tong, Tiejun; Landers, John; Wu, Zheyang: TFisher: a powerful truncation and weighting procedure for combining (p)-values (2020)
  15. Alqawba, Mohammed; Diawara, Norou; Rao Chaganty, N.: Zero-inflated count time series models using Gaussian copula (2019)
  16. Azaïs, Jean-Marc; Mourareau, Stéphane: How sharp are classical approximations for statistical applications? (2019)
  17. Cao, Jian; Genton, Marc G.; Keyes, David E.; Turkiyyah, George M.: Hierarchical-block conditioning approximations for high-dimensional multivariate normal probabilities (2019)
  18. Lindgren, Georg: Gaussian integrals and Rice series in crossing distributions -- to compute the distribution of maxima and other features of Gaussian processes (2019)
  19. Liu, Zhonghua; Lin, Xihong: A geometric perspective on the power of principal component association tests in multiple phenotype studies (2019)
  20. Nomura, Noboru: Orthant probabilities of elliptical distributions from orthogonal projections to subspaces (2019)

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