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A Fast Metropolis-Hastings Method for Generating Random Correlation Matrices. / Córdoba, Irene; Varando, Gherardo; Bielza, Concha; Larrañaga, Pedro.
Distributions and operators Gerd Grubb: 19th International Conference Madrid, Spain, November 21–23, 2018. red. / Hujun Yin; David Camacho; Paulo Novais; Antonio J. Tallón-Ballesteros. Bind 1 Springer, 2018. s. 117-124 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 11314 LNCS).
Publikation: Bidrag til bog/antologi/rapport › Bidrag til bog/antologi › Forskning › fagfællebedømt
Harvard
Córdoba, I, Varando, G, Bielza, C & Larrañaga, P 2018,
A Fast Metropolis-Hastings Method for Generating Random Correlation Matrices. i H Yin, D Camacho, P Novais & AJ Tallón-Ballesteros (red),
Distributions and operators Gerd Grubb: 19th International Conference Madrid, Spain, November 21–23, 2018. bind 1, Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), bind 11314 LNCS, s. 117-124, 19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018, Madrid, Spanien,
21/11/2018.
https://doi.org/10.1007/978-3-030-03493-1_13APA
Córdoba, I., Varando, G., Bielza, C., & Larrañaga, P. (2018).
A Fast Metropolis-Hastings Method for Generating Random Correlation Matrices. I H. Yin, D. Camacho, P. Novais, & A. J. Tallón-Ballesteros (red.),
Distributions and operators Gerd Grubb: 19th International Conference Madrid, Spain, November 21–23, 2018 (Bind 1, s. 117-124). Springer. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Bind 11314 LNCS
https://doi.org/10.1007/978-3-030-03493-1_13Vancouver
Córdoba I, Varando G, Bielza C, Larrañaga P.
A Fast Metropolis-Hastings Method for Generating Random Correlation Matrices. I Yin H, Camacho D, Novais P, Tallón-Ballesteros AJ, red., Distributions and operators Gerd Grubb: 19th International Conference Madrid, Spain, November 21–23, 2018. Bind 1. Springer. 2018. s. 117-124. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 11314 LNCS).
https://doi.org/10.1007/978-3-030-03493-1_13Author
Córdoba, Irene ; Varando, Gherardo ; Bielza, Concha ; Larrañaga, Pedro. / A Fast Metropolis-Hastings Method for Generating Random Correlation Matrices. Distributions and operators Gerd Grubb: 19th International Conference Madrid, Spain, November 21–23, 2018. red. / Hujun Yin ; David Camacho ; Paulo Novais ; Antonio J. Tallón-Ballesteros. Bind 1 Springer, 2018. s. 117-124 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 11314 LNCS).
Bibtex
@inbook{b2e7054c433647dc95c941a956b48135,
title = "A Fast Metropolis-Hastings Method for Generating Random Correlation Matrices",
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.",
keywords = "Correlation matrices, Metroplis-Hastings, Random sampling",
author = "Irene C{\'o}rdoba and Gherardo Varando and Concha Bielza and Pedro Larra{\~n}aga",
year = "2018",
doi = "10.1007/978-3-030-03493-1_13",
language = "English",
isbn = "9783030034924",
volume = "1",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "117--124",
editor = "Yin, {Hujun } and Camacho, {David } and Novais, {Paulo } and Tall{\'o}n-Ballesteros, {Antonio J. }",
booktitle = "Distributions and operators Gerd Grubb",
address = "Switzerland",
note = "19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018 ; Conference date: 21-11-2018 Through 23-11-2018",
}
RIS
TY - CHAP
T1 - A Fast Metropolis-Hastings Method for Generating Random Correlation Matrices
AU - Córdoba, Irene
AU - Varando, Gherardo
AU - Bielza, Concha
AU - Larrañaga, Pedro
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Correlation matrices
KW - Metroplis-Hastings
KW - Random sampling
UR - http://www.mendeley.com/research/fast-metropolishastings-method-generating-random-correlation-matrices
U2 - 10.1007/978-3-030-03493-1_13
DO - 10.1007/978-3-030-03493-1_13
M3 - Book chapter
SN - 9783030034924
VL - 1
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 117
EP - 124
BT - Distributions and operators Gerd Grubb
A2 - Yin, Hujun
A2 - Camacho, David
A2 - Novais, Paulo
A2 - Tallón-Ballesteros, Antonio J.
PB - Springer
T2 - 19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018
Y2 - 21 November 2018 through 23 November 2018
ER -
