A statistical inference framework for day-to-day traffic dynamics models allows identifiability, consistency proofs, and parameter estimation from trajectory data, with extensions for heterogeneity and privacy, validated on simulations and Ann Arbor data.
arXiv preprint arXiv:2201.04237 , year=
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A hierarchical shrinkage model is introduced for node-parent conditional probabilities in discrete Bayesian networks, enabling posterior sampling and structure learning that handles sparse counts.
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Statistical Inference of Day-to-Day Traffic Dynamics
A statistical inference framework for day-to-day traffic dynamics models allows identifiability, consistency proofs, and parameter estimation from trajectory data, with extensions for heterogeneity and privacy, validated on simulations and Ann Arbor data.
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Learning discrete Bayesian networks with hierarchical Dirichlet shrinkage
A hierarchical shrinkage model is introduced for node-parent conditional probabilities in discrete Bayesian networks, enabling posterior sampling and structure learning that handles sparse counts.