trend filtering

Thursday, April 26, 2018 - 1:30pm - 2:00pm
Daniel Kowal (Rice University)
We propose a novel class of dynamic shrinkage processes for Bayesian time series and regression analysis. Building upon the global-local framework of prior construction, in which continuous scale mixtures of Gaussian distributions are employed for both desirable shrinkage properties and computational tractability, we allow the local scale parameters to depend on the history of the shrinkage process.
Subscribe to RSS - trend filtering