Bag of DAGs: Inferring Directional Dependence in Spatiotemporal Processes

  • Simple example demonstration

  • Propose a class of nonstationary processes that characterize varying directional associations in space and time for point-referenced data.

  • Place a prior over possible directional edges within sparse directed acyclic graphs (DAGs), accounting for uncertainty in directional correlation patterns across a domain.

  • The resulting Bag of DAGs processes (BAGs) lead to interpretable nonstationarity and scalability for large data due to sparsity of DAGs in the bag.

  • 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.