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. doi:10.1515/mcma.2000.6.3.205. https://resolver.caltech.edu/CaltechAUTHORS:20180803-081754849
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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 | ||||||
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| Additional Information: | © 2000 by Walter de Gruyter GmbH. | ||||||
| Subject Keywords: | latent roots, latent vectors, random sample | ||||||
| Issue or Number: | 3 | ||||||
| DOI: | 10.1515/mcma.2000.6.3.205 | ||||||
| Record Number: | CaltechAUTHORS:20180803-081754849 | ||||||
| Persistent URL: | https://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: | 16 Nov 2021 00:27 |
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