Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
Zur Ablage hinzufügen
 DetailsÜbersicht
  Inference algorithms and learning theory for Bayesian sparse factor analysis

Rattray, M., Stegle, O., Sharp, K., & Winn, J. (2009). Inference algorithms and learning theory for Bayesian sparse factor analysis. Journal of Physics: Conference Series, 197(1: International Workshop on Statistical-Mechanical Informatics 2009), 1-10. doi:10.1088/1742-6596/197/1/012002.

Item is

 

einblenden: alle ausblenden: alle

Basisdaten

einblenden: ausblenden:
Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C2EA-9 Versions-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C2EB-7
Genre: Zeitschriftenartikel

Externe Referenzen

einblenden:

Urheber

einblenden:
ausblenden:
 Urheber:
Rattray, M, Autor
Stegle, O1, Autor           
Sharp, K, Autor
Winn, J, Autor
Inoue, Herausgeber
M., Herausgeber
Ishii, S., Herausgeber
Kabashima, Y., Herausgeber
Okada, M., Herausgeber
Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

Inhalt

einblenden:
ausblenden:
Schlagwörter: -
 Zusammenfassung: Bayesian sparse factor analysis has many applications; for example, it has been applied to the problem of inferring a sparse regulatory network from gene expression data. We describe a number of inference algorithms for Bayesian sparse factor analysis using a slab and spike mixture prior. These include well-established Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms as well as a novel hybrid of VB and Expectation Propagation (EP). For the case of a single latent factor we derive a theory for learning performance using the replica method. We compare the MCMC and VB/EP algorithm results with simulated data to the theoretical prediction. The results for MCMC agree closely with the theory as expected. Results for VB/EP are slightly sub-optimal but show that the new algorithm is effective for sparse inference. In large-scale problems MCMC is infeasible due to computational limitations and the VB/EP algorithm then provides a very useful computationally efficient alternative.

Details

einblenden:
ausblenden:
Sprache(n):
 Datum: 2009-09
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: URI: http://www.iop.org/EJ/abstract/1742-6596/197/1/011001
DOI: 10.1088/1742-6596/197/1/012002
BibTex Citekey: 6296
 Art des Abschluß: -

Veranstaltung

einblenden:

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle 1

einblenden:
ausblenden:
Titel: Journal of Physics: Conference Series
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: Bristol, UK : Institute of Physics
Seiten: - Band / Heft: 197 (1: International Workshop on Statistical-Mechanical Informatics 2009) Artikelnummer: - Start- / Endseite: 1 - 10 Identifikator: -