Bayesian Approaches for Dynamic Model Selection: two contributions
Friday, October 27, 2017 - 3:30pm
In many applications, investigators consider processes that vary in space and time, with the goal of identifying temporally persistent and spatially localized departures of those processes from a baseline or ``normal" state. In this talk, I will first discuss a Bayesian nonparametric model selection approach for the analysis of spatio-temporal data, which takes into account the non-exchangeable nature of measurements collected over time and space. I will outline the model and discuss its performances by means of a simulation study and an application to a disease surveillance problem, for detecting outbreaks of pneumonia and influenza mortality in the continental United States. Then, I will discuss a principled Bayesian approach for estimating time varying functional connectivity networks from brain fMRI data. Dynamic functional connectivity, i.e., the study of how interactions among brain regions change dynamically over the course of an fMRI experiment, has recently received wide interest in the neuroimaging literature. Our method utilizes a hidden Markov model for classification of latent neurological states, achieving estimation of the connectivity networks in an integrated framework that borrows strength over the entire time course of the experiment. Furthermore, we assume that the graph structures, which define the connectivity states at each time point, are related within a super-graph, to encourage the selection of the same edges among related graphs. We analyze data from an fMRI sensorimotor task experiment on a healthy subject and obtain results that support the role of particular anatomical regions in modulating interaction between executive control and attention networks.
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