Efficient algorithms for generating truncated multivariate normal distributions

DOI10.1007/s10255-011-0110-xzbMath1272.62046OpenAlexW2088155199MaRDI QIDQ1942149

Guo-Liang Tian, Jun-Wu Yu

Publication date: 18 March 2013

Published in: Acta Mathematicae Applicatae Sinica. English Series (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10255-011-0110-x

zbMATH Keywords

linear inequality constraintsEM algorithmGibbs samplerdata augmentationIBF sampler

Mathematics Subject Classification ID

Multivariate distribution of statistics (62H10) Linear regression; mixed models (62J05) Bayesian inference (62F15)

Related Items (6)

Unimodal regression using Bernstein–Schoenberg splines and penalties ⋮ Bayesian regression with B‐splines under combinations of shape constraints and smoothness properties ⋮ A New Rejection Sampling Method for Truncated Multivariate Gaussian Random Variables Restricted to Convex Sets ⋮ EM algorithm of the truncated multinormal distribution with linear restriction on the variables ⋮ Bayesian spatial prediction of skew and censored data via a hybrid algorithm ⋮ Efficient sampling methods for truncated multivariate normal and Student-\(t\) distributions subject to linear inequality constraints

Uses Software

• GAUSS
• Matlab
• S-PLUS
• BayesDA

Cites Work

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