CaltechAUTHORS
  A Caltech Library Service

Factorization of Separable and Patterned Covariance Matrices for Gibbs Sampling

Rowe, Daniel B. (2000) Factorization of Separable and Patterned Covariance Matrices for Gibbs Sampling. Monte Carlo Methods and Applications, 6 (3). pp. 205-210. ISSN 0929-9629. http://resolver.caltech.edu/CaltechAUTHORS:20180803-081754849

Full text is not posted in this repository. Consult Related URLs below.

Use this Persistent URL to link to this item: http://resolver.caltech.edu/CaltechAUTHORS:20180803-081754849

Abstract

Recently the Gibbs sample has become a very popular estimation technique especially in Bayesian Statistics. In order to implement the Gibbs sampler, matrix factorization must be computed which normally is not problematic. When the dimension of the matrices to be factored is large, computation time increases to an amount to merit special attention. I have found that when the matrices to be factored are separable or patterned, results from matrix theory can assist in computation time reduction.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1515/mcma.2000.6.3.205DOIArticle
Additional Information:© 2000 by Walter de Gruyter GmbH.
Subject Keywords:latent roots, latent vectors, random sample
Record Number:CaltechAUTHORS:20180803-081754849
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20180803-081754849
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:88556
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:03 Aug 2018 17:34
Last Modified:03 Aug 2018 17:34

Repository Staff Only: item control page