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.
Goncalves, and Jakob H
9 Pith papers cite this work. Polarity classification is still indexing.
9
Pith papers citing it
years
2026 9verdicts
UNVERDICTED 9representative citing papers
SPIN improves posterior inference under model misspecification in SBI by learning parameter-relevant information-preserving domain transfers from unpaired unlabeled real-world data.
A multimodal amortized neural posterior estimator trained on realistic simulations recovers DEB parameters accurately with calibrated uncertainties on held-out tests.
FLUID uses a recurrent encoder to create a fixed-size summary of observations, then learns coupled forward and backward flows to approximate filtering distributions and recover smoothing paths for nonlinear dynamics, with support for extrapolation.
Neural posterior estimation trained on simulated radar data enables probabilistic inference of terrain parameters from real Mars radar sounder profiles while conditioning on reference surface assumptions.
Amortized neural posterior estimation via simulation-based inference delivers 82x faster inference than MCMC for heat exchanger fouling and leakage diagnosis while maintaining comparable accuracy on synthetic data.
Simulation-based inference reliably extracts physical parameters from noisy spectra of analogue black holes.
NPE delivers millisecond-scale parameter inference for Li-ion batteries that matches or exceeds Bayesian calibration accuracy while adding local sensitivity interpretability, though with higher voltage prediction errors.
Simulation-based inference uses neural networks trained on simulations to enable parameter inference in cosmology and astrophysics where traditional likelihood calculations are intractable.
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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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.
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Neural Simulation-based Inference with Hierarchical Priors for Detached Eclipsing Binaries
A multimodal amortized neural posterior estimator trained on realistic simulations recovers DEB parameters accurately with calibrated uncertainties on held-out tests.
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FLUID: Flow-based Unified Inference for Dynamics
FLUID uses a recurrent encoder to create a fixed-size summary of observations, then learns coupled forward and backward flows to approximate filtering distributions and recover smoothing paths for nonlinear dynamics, with support for extrapolation.
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Neural Posterior Estimation of Terrain Parameters from Radar Sounder Data
Neural posterior estimation trained on simulated radar data enables probabilistic inference of terrain parameters from real Mars radar sounder profiles while conditioning on reference surface assumptions.
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Fast Bayesian equipment condition monitoring via simulation based inference: applications to heat exchanger health
Amortized neural posterior estimation via simulation-based inference delivers 82x faster inference than MCMC for heat exchanger fouling and leakage diagnosis while maintaining comparable accuracy on synthetic data.
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Spectroscopy of analogue black holes using simulation-based inference
Simulation-based inference reliably extracts physical parameters from noisy spectra of analogue black holes.
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Neural posterior estimation for scalable and accurate inverse parameter inference in Li-ion batteries
NPE delivers millisecond-scale parameter inference for Li-ion batteries that matches or exceeds Bayesian calibration accuracy while adding local sensitivity interpretability, though with higher voltage prediction errors.
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Machine Learning Techniques for Astrophysics and Cosmology: Simulation-Based Inference
Simulation-based inference uses neural networks trained on simulations to enable parameter inference in cosmology and astrophysics where traditional likelihood calculations are intractable.