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Wrapped Distributions on homogeneous Riemannian manifolds
We provide a general framework for constructing probability distributions on Riemannian manifolds, taking advantage of area-preserving maps and isometries.
Bayesian Model Averaging for Data Driven Decision Making when Causality is Partially Known
Probabilistic machine learning models are often insufficient to help with decisions on interventions because those models find correlations - not causal relationships.
Relational Dynamic Bayesian Network Modeling for Uncertainty Quantification and Propagation in Airline Disruption Management
Disruption management during the airline scheduling process can be compartmentalized into proactive and reactive processes depending upon the time of schedule execution.
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