title: A fast Metropolis-Hastings method for generating random correlation matrices
creator: Córdoba Sánchez, Irene
creator: Varando, Gherardo
creator: Bielza Lozoya, María Concepción
creator: Larrañaga Múgica, Pedro María
subject: Computer Science
description: We propose a novel Metropolis-Hastings algorithm to sample uniformly from the space of correlation matrices. Existing methods in the literature are based on elaborated representations of a correlation matrix, or on complex parametrizations of it. By contrast, our method is intuitive and simple, based the classical Cholesky factorization of a positive definite matrix and Markov chain Monte Carlo theory. We perform a detailed convergence analysis of the resulting Markov chain, and show how it benefits from fast convergence, both theoretically and empirically. Furthermore, in numerical experiments our algorithm is shown to be significantly faster than the current alternative approaches, thanks to its simple yet principled approach.
publisher: E.T.S. de Ingenieros Informáticos (UPM)
rights: https://creativecommons.org/licenses/by-nc-nd/3.0/es/
date: 2018
type: info:eu-repo/semantics/conferenceObject
type: Presentation at Congress or Conference
source: Intelligent Data Engineering and Automated Learning (IDEAL 2018) | 19th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2018) | 21-23 Nov 2018 | Madrid, España
type: PeerReviewed
format: application/pdf
language: eng
relation: https://link.springer.com/chapter/10.1007/978-3-030-03493-1_13
relation: info:eu-repo/grantAgreement/MINECO//C080020-09
relation: info:eu-repo/grantAgreement/MINECO//TIN2016-79684-P
relation: info:eu-repo/grantAgreement/CAM//S2013%2FICE-2845
rights: info:eu-repo/semantics/openAccess
relation: info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-030-03493-1_13
identifier: https://oa.upm.es/54634/