Wilson, AG and Ghahramani, Z (2010) Generalised Wishart Processes. Uncertainty in Artificial Intelligence (2011).Full text not available from this repository.
We introduce a stochastic process with Wishart marginals: the generalised Wishart process (GWP). It is a collection of positive semi-definite random matrices indexed by any arbitrary dependent variable. We use it to model dynamic (e.g. time varying) covariance matrices. Unlike existing models, it can capture a diverse class of covariance structures, it can easily handle missing data, the dependent variable can readily include covariates other than time, and it scales well with dimension; there is no need for free parameters, and optional parameters are easy to interpret. We describe how to construct the GWP, introduce general procedures for inference and predictions, and show that it outperforms its main competitor, multivariate GARCH, even on financial data that especially suits GARCH. We also show how to predict the mean of a multivariate process while accounting for dynamic correlations.
|Divisions:||Div F > Computational and Biological Learning|
|Depositing User:||Unnamed user with email firstname.lastname@example.org|
|Date Deposited:||09 Dec 2016 17:39|
|Last Modified:||30 Mar 2017 00:11|