PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Generalised Wishart Processes
Andrew Wilson and Zoubin Ghahramani
In: 27th Conference on Uncertainty in Artificial Intelligence, 14-17 July 2011, Barcelona, Spain.

Abstract

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. 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.

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EPrint Type:Conference or Workshop Item (Oral)
Additional Information:Best Student Paper Award
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:8462
Deposited By:Andrew Wilson
Deposited On:21 January 2012