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Found 4 papers, 1 papers with code
A Bayesian Approach to Reconstructing Interdependent Infrastructure Networks from Cascading Failures
We propose a scalable nonparametric Bayesian approach to reconstruct the topology of interdependent infrastructure networks from observations of cascading failures.
Approximate Gibbs Sampler for Efficient Inference of Hierarchical Bayesian Models for Grouped Count Data
Hierarchical Bayesian Poisson regression models (HBPRMs) provide a flexible modeling approach of the relationship between predictors and count response variables.
Learning Incident Prediction Models Over Large Geographical Areas for Emergency Response Systems
These statistical models are then used for proactive stationing which allocates first responders across the spatial area in order to reduce overall response time.
A Review of Incident Prediction, Resource Allocation, and Dispatch Models for Emergency Management
In the last fifty years, researchers have developed statistical, data-driven, analytical, and algorithmic approaches for designing and improving emergency response management (ERM) systems.
