We are very excited to join forces with MLCommons and OctoML.ai! Contact Grigori Fursin for more details!

Copula Processes

lib:e294def344d2b333 (v1.0.0)

Authors: Andrew G. Wilson,Zoubin Ghahramani
Where published: NeurIPS 2010 12
Document:  PDF  DOI 
Abstract URL: http://papers.nips.cc/paper/4082-copula-processes


We define a copula process which describes the dependencies between arbitrarily many random variables independently of their marginal distributions. As an example, we develop a stochastic volatility model, Gaussian Copula Process Volatility (GCPV), to predict the latent standard deviations of a sequence of random variables. To make predictions we use Bayesian inference, with the Laplace approximation, and with Markov chain Monte Carlo as an alternative. We find our model can outperform GARCH on simulated and financial data. And unlike GARCH, GCPV can easily handle missing data, incorporate covariates other than time, and model a rich class of covariance structures.

Relevant initiatives  

Related knowledge about this paper Reproduced results (crowd-benchmarking and competitions) Artifact and reproducibility checklists Common formats for research projects and shared artifacts Reproducibility initiatives

Comments  

Please log in to add your comments!
If you notice any inapropriate content that should not be here, please report us as soon as possible and we will try to remove it within 48 hours!