Machine Learning: Regression whith stochastic volatility

I had gotten there by a long search that had gone from machine learning, to fast Kalman filters, to Bayesian conjugate linear regression, to representing uncertainty in the covariance using an inverse Wishart prior, to making it time-varying, and allowing heteroschedasticity. I was thinking whith my koukio friends and this paper had all the pieces […]