Speaker
Description
We present a hybrid graph-based Bayesian framework for uncertainty-aware inference of rare events on spatially embedded networks.
Starting from a Multivariate Conditional Autoregressive Model (MCAR) in a hierarchical Bayesian architecture defined on the dual graph of a street network, events counts are described by Poisson likelihoods with structured graph effects, unstructured heterogeneity and node-level covariates. The approach was validated on georeferenced accidents data from Rome, where it enabled segment-level latent-risk estimation, crashes severity correlation modeling and calibrated posterior uncertainty.
We propose a physics-oriented extension combining this Bayesian graphical model with GNN-based amortized variational inference. A graph neural encoder learns local and non-local representations and parametrizes variational posteriors or Bayesian hyperparameters, without doing the optimization of the parameters for each observation. Meanwhile, the probabilistic layer preserves interpretable likelihoods, structured priors and uncertainty quantification. This hybrid formulation is scalable beyond MCMC and naturally suited to sparse, noisy, high-dimensional graphs. Potential applications include charged-particle tracking, vertexing, calorimeter clustering, pileup mitigation, jet anomaly detection, continuous gravitational-wave candidate clustering, astroparticle source association and multi-messenger event correlation.