Extends NPE to mixed discrete-continuous parameter spaces via a factorized inference network combining an autoregressive classifier and generative model, trained jointly to yield accurate calibrated posteriors.
Investigating the impact of model misspecification in neural simulation-based inference.arXiv preprint arXiv:2209.01845,
3 Pith papers cite this work. Polarity classification is still indexing.
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Dingo-Pop uses a transformer to perform amortized, end-to-end population inference from GW strain data in seconds, bypassing per-event Monte Carlo sampling.
SPIN improves posterior inference under model misspecification in SBI by learning parameter-relevant information-preserving domain transfers from unpaired unlabeled real-world data.
citing papers explorer
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Mixed neural posterior estimation for simulators with discrete and continuous parameters
Extends NPE to mixed discrete-continuous parameter spaces via a factorized inference network combining an autoregressive classifier and generative model, trained jointly to yield accurate calibrated posteriors.
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End-to-End Population Inference from Gravitational-Wave Strain using Transformers
Dingo-Pop uses a transformer to perform amortized, end-to-end population inference from GW strain data in seconds, bypassing per-event Monte Carlo sampling.
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Information-Preserving Domain Transfer with Unlabeled Data in Misspecified Simulation-Based Inference
SPIN improves posterior inference under model misspecification in SBI by learning parameter-relevant information-preserving domain transfers from unpaired unlabeled real-world data.