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.
Bioinformatics , volume =
2 Pith papers cite this work. Polarity classification is still indexing.
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Modeling three labels in five-label lipid metabolism experiments balances parameter estimation accuracy, trajectory recovery, and computational cost better than using one or all labels.
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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.
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Balancing label resolution and computational cost in dynamical models of lipid metabolism
Modeling three labels in five-label lipid metabolism experiments balances parameter estimation accuracy, trajectory recovery, and computational cost better than using one or all labels.