Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes

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 ofamultivariate timeseries.