Generalised Wishart processes

Andrew Gordon Wilson, Zoubin Ghahramani

Research output: Contribution to conferencePaperpeer-review


We introduce a new stochastic process called the generalised Wishart process (GWP). It is a collection of positive semi-definite random matrices indexed by any arbitrary input variable. We use this process as a prior over dynamic (e.g. time varying) covariance matrices Σ(t). The GWP captures a diverse class of covariance dynamics, naturally handles missing data, scales nicely with dimension, has easily interpretable parameters, and can use input variables that include covariates other than time. We describe how to construct the GWP, introduce general procedures for inference and prediction, and show that it outperforms its main competitor, multivariate GARCH, even on financial data that especially suits GARCH.

Original languageEnglish (US)
Number of pages9
StatePublished - 2011
Event27th Conference on Uncertainty in Artificial Intelligence, UAI 2011 - Barcelona, Spain
Duration: Jul 14 2011Jul 17 2011


Other27th Conference on Uncertainty in Artificial Intelligence, UAI 2011

ASJC Scopus subject areas

  • Artificial Intelligence
  • Applied Mathematics


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