Random sampling from a truncated multivariate normal distribution

DOI10.1016/0893-9659(94)90042-6zbMath0795.62060OpenAlexW2064321518MaRDI QIDQ1324429

Jon A. Breslaw

Publication date: 18 September 1994

Published in: Applied Mathematics Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0893-9659(94)90042-6

zbMATH Keywords

simulationGibbs samplerGauss codetruncated multivariate normal distributiontruncated bivariate normal distribution

Mathematics Subject Classification ID

Multivariate analysis (62H99) Software, source code, etc. for problems pertaining to statistics (62-04) Statistical distribution theory (62E99)

Related Items (8)

A New Rejection Sampling Method for Truncated Multivariate Gaussian Random Variables Restricted to Convex Sets ⋮ Efficient algorithms for generating truncated multivariate normal distributions ⋮ Efficient simulation for dependent rare events with applications to extremes ⋮ Nonlinear mixed-effects HIV dynamic models with considering left-censored measurements ⋮ Mixed Effects Models with Censored Data with Application to HIV RNA Levels ⋮ Joint model of accelerated failure time and mechanistic nonlinear model for censored covariates, with application in HIV/AIDS ⋮ Assessing phenotypic correlation through the multivariate phylogenetic latent liability model ⋮ Efficient sampling methods for truncated multivariate normal and Student-\(t\) distributions subject to linear inequality constraints

Cites Work

• Random sampling from a truncated bivariate normal distribution
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• The Calculation of Posterior Distributions by Data Augmentation

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