Bayesian Dynamic Modeling: Sharing Information Across Time and Space

This talk will highlight some of the benefits and challenges associated with harnessing the temporal structure present in many datasets. The focus is on Bayesian dynamic modeling approaches, and in particular, the idea of sharing information across time and "space," where space generically refers to the dimensions of the time series. Emily Fox, UW Assistant Professor of Statistics, discusses how to exploit nonparametric and hierarchical models to capture repeated patterns in time and similar structure in space, enabling the modeling of complex and high-dimensional time series. Applications of such approaches are quite diverse, and she demonstrate this by touching upon work in the tasks of speaker diarization, analyzing human motion, detecting changes in volatility of stock indices, parsing EEG, word classification from MEG, and predicting rates of violent crimes in DC and influenza rates in the US.