Modelling Input Varying Correlations Between Multiple Responses
Abstract
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.
Cite
Text
Wilson and Ghahramani. "Modelling Input Varying Correlations Between Multiple Responses." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2012. doi:10.1007/978-3-642-33486-3_64Markdown
[Wilson and Ghahramani. "Modelling Input Varying Correlations Between Multiple Responses." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2012.](https://mlanthology.org/ecmlpkdd/2012/wilson2012ecmlpkdd-modelling/) doi:10.1007/978-3-642-33486-3_64BibTeX
@inproceedings{wilson2012ecmlpkdd-modelling,
title = {{Modelling Input Varying Correlations Between Multiple Responses}},
author = {Wilson, Andrew Gordon and Ghahramani, Zoubin},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2012},
pages = {858-861},
doi = {10.1007/978-3-642-33486-3_64},
url = {https://mlanthology.org/ecmlpkdd/2012/wilson2012ecmlpkdd-modelling/}
}