Anzeige
Erschienen in:
2012 | OriginalPaper | Buchkapitel
Modelling Input Varying Correlations between Multiple Responses
verfasst von : Andrew Gordon Wilson, Zoubin Ghahramani
Erschienen in: Machine Learning and Knowledge Discovery in Databases
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
We introduced a generalised Wishart process (GWP) for modelling input dependent covariance matrices Σ(
x
), allowing one to model input varying correlations and uncertainties between multiple response variables. The GWP can naturally scale to thousands of response variables, as opposed to competing
multivariate volatility
models which are typically intractable for greater than 5 response variables. The GWP can also naturally capture a rich class of covariance dynamics – periodicity, Brownian motion, smoothness, …– through a covariance kernel.