Bayesian Analysis

Fast Simulation of Hyperplane-Truncated Multivariate Normal Distributions

Yulai Cong, Bo Chen, and Mingyuan Zhou

Full-text: Open access

Abstract

We introduce a fast and easy-to-implement simulation algorithm for a multivariate normal distribution truncated on the intersection of a set of hyperplanes, and further generalize it to efficiently simulate random variables from a multivariate normal distribution whose covariance (precision) matrix can be decomposed as a positive-definite matrix minus (plus) a low-rank symmetric matrix. Example results illustrate the correctness and efficiency of the proposed simulation algorithms.

Article information

Source
Bayesian Anal. Volume 12, Number 4 (2017), 1017-1037.

Dates
First available in Project Euclid: 1 March 2017

Permanent link to this document
https://projecteuclid.org/euclid.ba/1488337478

Digital Object Identifier
doi:10.1214/17-BA1052

Keywords
Cholesky decomposition conditional distribution equality constraints high-dimensional regression structured covariance/precision matrix

Rights
Creative Commons Attribution 4.0 International License.

Citation

Cong, Yulai; Chen, Bo; Zhou, Mingyuan. Fast Simulation of Hyperplane-Truncated Multivariate Normal Distributions. Bayesian Anal. 12 (2017), no. 4, 1017--1037. doi:10.1214/17-BA1052. https://projecteuclid.org/euclid.ba/1488337478


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