A hierarchical Bayesian framework pools information across sparse dynamical system datasets via a shared population distribution to improve parameter inference and prediction over unpooled approaches.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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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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Learning Dynamical Systems from Multiple Sparse Datasets: A Hierarchical Bayesian Modeling Approach
A hierarchical Bayesian framework pools information across sparse dynamical system datasets via a shared population distribution to improve parameter inference and prediction over unpooled approaches.
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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.