2019
Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes
NeurIPS 2019poster
We implement gradient-based variational inference routines for Wishart and inverse Wishart processes, which we apply as Bayesian models for the dynamic, heteroskedastic covariance matrix of a multivariate time series. The Wishart and inverse Wishart processes are constructed from i.i.d. Gaussian pro…