PuSH - Publikationsserver des Helmholtz Zentrums München

Export: TXT Text RIS Endnote (RIS) BIB BibTeX
Schmidl, D. ; Czado, C.* ; Hug, S. ; Theis, F.J.

A vine-copula based adaptive MCMC sampler for efficient inference of dynamical systems.

Bayesian Anal. 8, 1-22 (2013)
Verlagsversion Volltext DOI
Open Access Gold
Statistical inference in high dimensional dynamical systems is often hindered by the unknown dependency structure of model parameters. In particu- lar, the inference of parameterized differential equations (DEs) via Markov chain Monte Carlo (MCMC) samplers often suffers from high proposal rejection rates and is exacerbated by strong autocorrelation structures within the Markov chains leading to poor mixing properties. In this paper, we develop a novel vine-copula based adaptive MCMC approach for efficient parameter inference in dynamical systems with strong parameter interdependence. We exploit the concept of a vine-copula decomposition of distribution densities in order to generate problem- specific proposals for a hybrid independence/random walk Metropolis-Hastings (MH) sampler. The key advantage of this approach is that the corresponding MH proposals generate independent samples from the posterior distribution more effi- ciently than common competitors. All copula densities can be updated during the sampling procedure for fine-tuning. The performance of our method is assessed on two small-scale examples and finally evaluated on a delay DE model for the JAK2-STAT5 signaling pathway fitted to time-resolved western blot data. We compare our copula-based approach to an independence sampler, a second-order moment-based random walk MH algorithm, and an adaptive MH sampler.
Impact Factor
Scopus SNIP
Web of Science
Times Cited
Scopus
Cited By
Altmetric
2.417
1.897
14
17
Tags
Icb_biostatistics Icb_Latent Causes Icb_ML
Anmerkungen
Besondere Publikation
Auf Hompepage verbergern

Zusatzinfos bearbeiten
Eigene Tags bearbeiten
Privat
Eigene Anmerkung bearbeiten
Privat
Auf Publikationslisten für
Homepage nicht anzeigen
Als besondere Publikation
markieren
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Parameter inference; Metropolis-Hastings algorithm; Independence sampling; Adaptive MCMC; Vine; Copula; Dependent Random-variables ; Metropolis Algorithm ; Markov-chains ; Constructions ; Model ; Decomposition
Sprache englisch
Veröffentlichungsjahr 2013
HGF-Berichtsjahr 2013
ISSN (print) / ISBN 1931-6690
e-ISSN 1931-6690
Zeitschrift Bayesian Analysis
Quellenangaben Band: 8, Heft: 1, Seiten: 1-22 Artikelnummer: , Supplement: ,
Verlag Carnegie Mellon Univ.
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Bioinformatics and Systems Biology (IBIS)
POF Topic(s) 30505 - New Technologies for Biomedical Discoveries
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-503700-004
DOI 10.1214/13-BA801
WOS ID WOS:000317572900001
Erfassungsdatum 2013-05-07
[➜Korrektur melden]