A fast Metropolis-Hastings method for generating random correlation matrices

Córdoba Sánchez, Irene and Varando, Gherardo and Bielza Lozoya, María Concepción and Larrañaga Múgica, Pedro María (2018). A fast Metropolis-Hastings method for generating random correlation matrices. In: "19th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2018)", 21-23 Nov 2018, Madrid, España. ISBN 978-3-030-03492-4. pp. 117-124. https://doi.org/10.1007/978-3-030-03493-1_13.

Description

Title: A fast Metropolis-Hastings method for generating random correlation matrices
Author/s:
  • Córdoba Sánchez, Irene
  • Varando, Gherardo
  • Bielza Lozoya, María Concepción
  • Larrañaga Múgica, Pedro María
Item Type: Presentation at Congress or Conference (Article)
Event Title: 19th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2018)
Event Dates: 21-23 Nov 2018
Event Location: Madrid, España
Title of Book: Intelligent Data Engineering and Automated Learning (IDEAL 2018)
Date: 2018
ISBN: 978-3-030-03492-4
Volume: 1
Subjects:
Freetext Keywords: Correlation matrices; Random sampling; Metroplis-Hastings
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainC080020-09UnspecifiedUnspecifiedCajal Blue Brain
Government of SpainTIN2016-79684-PUnspecifiedUniversidad Politécnica de MadridAvances en clasificación multidimensional y detección de anomalías con redes bayesianas
Madrid Regional GovernmentS2013/ICE-2845CASI – CAMUnspecifiedConceptos y aplicaciones de los sistemas inteligentes

More information

Item ID: 54634
DC Identifier: http://oa.upm.es/54634/
OAI Identifier: oai:oa.upm.es:54634
DOI: 10.1007/978-3-030-03493-1_13
Official URL: https://link.springer.com/chapter/10.1007/978-3-030-03493-1_13
Deposited by: Memoria Investigacion
Deposited on: 29 Apr 2019 10:40
Last Modified: 06 May 2019 07:11
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