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Propose a class of nonstationary processes that characterize varying directional associations in space and time for point-referenced data.
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Place a prior over possible directional edges within sparse directed acyclic graphs (DAGs), accounting for uncertainty in directional correlation patterns across a domain.
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The resulting Bag of DAGs processes (BAGs) lead to interpretable nonstationarity and scalability for large data due to sparsity of DAGs in the bag.
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Analyze spatiotemporal variability of fine particulate matter (PM2.5) in California, US, in which a directed edge represents a prevailing wind direction causing some associated covariance in the pollutants.