%0 Conference Paper
%A Córdoba Sánchez, Irene
%A Varando, Gherardo
%A Bielza Lozoya, María Concepción
%A Larrañaga Múgica, Pedro María
%B 19th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2018)
%C Madrid, España
%D 2018
%F upm:54634
%I Springer
%K Correlation matrices; Random sampling; Metroplis-Hastings
%P 117-124
%R 10.1007/978-3-030-03493-1_13
%T A fast Metropolis-Hastings method for generating random correlation matrices
%U https://oa.upm.es/54634/
%V 1
%X 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.