Embedding selection mechanisms into generative simulators enables amortized Bayesian inference to produce debiased, well-calibrated posteriors without tractable likelihoods.
pyPESTO: a modular and scalable tool for parameter estimation for dynamic models
2 Pith papers cite this work, alongside 54 external citations. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
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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Overcoming Selection Bias in Statistical Studies With Amortized Bayesian Inference
Embedding selection mechanisms into generative simulators enables amortized Bayesian inference to produce debiased, well-calibrated posteriors without tractable likelihoods.
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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.