Bayes-THIS applies sparse Bayesian regression with automatic relevance determination to infer hypergraph structure from dynamical data and proves that Taylor expansions create indistinguishable spurious pairwise terms when higher-order interactions concentrate on nodes lacking lower-order links.
Bick , author E
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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The authors establish consistent and asymptotically normal estimators for parameters in composite birth-death processes via conditional likelihood under a Doob h-transform, plus a test for higher-order mechanisms.
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Bayesian hypergraph inference from scarce and noisy dynamical observations
Bayes-THIS applies sparse Bayesian regression with automatic relevance determination to infer hypergraph structure from dynamical data and proves that Taylor expansions create indistinguishable spurious pairwise terms when higher-order interactions concentrate on nodes lacking lower-order links.
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Likelihood-based inference for birth-death processes with composite birth mechanisms
The authors establish consistent and asymptotically normal estimators for parameters in composite birth-death processes via conditional likelihood under a Doob h-transform, plus a test for higher-order mechanisms.