TY - GEN
T1 - Generalised Wishart processes
AU - Wilson, Andrew Gordon
AU - Ghahramani, Zoubin
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=80053163503&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80053163503&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:80053163503
T3 - Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011
SP - 736
EP - 744
BT - Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011
PB - AUAI Press
ER -