  We consider the scenario where the parameters of a probabilistic model are
expected to vary over time. We construct a novel prior distribution that
promotes sparsity and adapts the strength of correlation between parameters at
successive timesteps, based on the data. We derive approximate variational
inference procedures for learning and prediction with this prior. We test the
approach on two tasks: forecasting financial quantities from relevant text, and
modeling language contingent on time-varying financial measurements.
