Derives Wasserstein bounds and explicit hyperparameter tuning rules for annealed Langevin dynamics in compositional score-based SBI, proving Linhart et al. (2026) allows larger steps and fewer total steps than Geffner et al. (2023) in the Gaussian case.
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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.
SPIN performs bidirectional domain transfer in SBI to retain parameter mutual information from unlabeled real observations, improving real-world posterior inference under increasing misspecification.
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.
A neural posterior estimator trained on simulated LISA foreground spectra recovers galactic binary population parameters, including total number, with good accuracy in validation tests.
Auto-encoder compression of X-ray spectra with multi-round neural posterior estimation and likelihood-based importance sampling yields posteriors statistically indistinguishable from nested sampling at roughly 10x speedup.
Optimization Monte Carlo reformulates stochastic simulator inference as gradient-based deterministic optimization for faster, accurate posterior estimation in high-dimensional or challenging settings.
OpenSeisML curates public seismic surveys into a reproducible dataset for training generative models that produce multiple statistically consistent subsurface realizations to support uncertainty-aware seismic inversion.
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.
A synthesis of diffusion-based simulation-based inference methods that address model misspecification, irregular observations, and missing data in scientific applications.
citing papers explorer
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Theoretical guidelines for annealed Langevin dynamics in compositional simulation-based inference
Derives Wasserstein bounds and explicit hyperparameter tuning rules for annealed Langevin dynamics in compositional score-based SBI, proving Linhart et al. (2026) allows larger steps and fewer total steps than Geffner et al. (2023) in the Gaussian case.
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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 performs bidirectional domain transfer in SBI to retain parameter mutual information from unlabeled real observations, improving real-world posterior inference under increasing misspecification.
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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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Inferring the population properties of galactic binaries from LISA's stochastic foreground
A neural posterior estimator trained on simulated LISA foreground spectra recovers galactic binary population parameters, including total number, with good accuracy in validation tests.
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Simulation-based inference with neural posterior estimation applied to X-ray spectral fitting -- III Deriving exact posteriors with dimension reduction and importance sampling
Auto-encoder compression of X-ray spectra with multi-round neural posterior estimation and likelihood-based importance sampling yields posteriors statistically indistinguishable from nested sampling at roughly 10x speedup.
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Fast and Robust Simulation-Based Inference With Optimization Monte Carlo
Optimization Monte Carlo reformulates stochastic simulator inference as gradient-based deterministic optimization for faster, accurate posterior estimation in high-dimensional or challenging settings.
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OpenSeisML: Open Large-Scale Real Seismic and well-log Dataset for Generative AI
OpenSeisML curates public seismic surveys into a reproducible dataset for training generative models that produce multiple statistically consistent subsurface realizations to support uncertainty-aware seismic inversion.
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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.
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A Review of Diffusion-based Simulation-Based Inference: Foundations and Applications in Non-Ideal Data Scenarios
A synthesis of diffusion-based simulation-based inference methods that address model misspecification, irregular observations, and missing data in scientific applications.