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.
and Cordier, Laurent and
2 Pith papers cite this work. Polarity classification is still indexing.
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Proposes adaptive multiple importance sampling for robust Bayesian model evidence estimation under parameter non-identifiability, shown to outperform deterministic methods on ecological case studies while being cheaper than MCMC.
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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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Reliable model selection in the presence of parameter non-identifiability
Proposes adaptive multiple importance sampling for robust Bayesian model evidence estimation under parameter non-identifiability, shown to outperform deterministic methods on ecological case studies while being cheaper than MCMC.