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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Validating bayesian inference algorithms with simulation-based calibration
17 Pith papers cite this work. Polarity classification is still indexing.
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Pointwise metrics compress marginal spectra in multimodal inverse problems, and a three-part protocol using CRPS, spectrum fidelity, and calibration reverses model rankings on synthetic and particle-physics benchmarks.
Bayesian joint model infers infectious virus shedding trajectories and derived infectiousness metrics from PCR and other proxies in SARS-CoV-2 using data from five cohorts of roughly 2000 infections.
SPIN performs bidirectional domain transfer in SBI to retain parameter mutual information from unlabeled real observations, improving real-world posterior inference under increasing misspecification.
Embedding selection mechanisms into generative simulators enables amortized Bayesian inference to produce debiased, well-calibrated posteriors without tractable likelihoods.
Simulation-based inference on Big Sobol Sequence halos at z=0.5 shows CMD+MFs improves σ8 and Ωm precision by ~27% over MFs alone and outperforms PS by ~45% in mass-selected samples at matched scales.
Score-based diffusion models learn the empirical distribution of real LIGO noise to enable unbiased gravitational-wave parameter estimation under only an additivity assumption.
New smooth invertible parameterization of anisotropic GF correlation length and diffusion matrix, with PC priors that penalize finite range and nonzero anisotropy for constant-parameter models.
Using ray-tracing simulations and simulation-based inference, the authors construct an AGN population that reproduces the cosmic X-ray background, number counts, and absorption properties, deriving an intrinsic Compton-thick fraction of 40±3%.
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.
MIRA is a new analytic score for conditional distribution accuracy derived from equal probability mass assignment, enabling Bayesian model comparison via direct posterior validation.
No evidence for deviations from general relativity is found in LIGO-Virgo binary black hole events, with improved constraints on waveform parameters, graviton mass, and ringdown properties.
Bayesian EVT with Hawkes-AR-Gumbel dependence estimates CVaR up to 99.995% on simulated operational risk data and outperforms independent and shared-factor baselines.
BILBY is validated on simulated compact binary signals and reproduces the eleven GWTC-1 results with configuration and output files provided for reproduction.
Simulation-based inference uses neural networks trained on simulations to enable parameter inference in cosmology and astrophysics where traditional likelihood calculations are intractable.
A review chapter groups machine learning methods for 21 cm cosmology by their pipeline roles in handling contaminated data, accelerating simulations, and inferring astrophysical parameters.
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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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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Pointwise Metrics Mislead: An Evaluation Protocol for Multimodal Inverse Problems
Pointwise metrics compress marginal spectra in multimodal inverse problems, and a three-part protocol using CRPS, spectrum fidelity, and calibration reverses model rankings on synthetic and particle-physics benchmarks.
-
Inferring infectiousness: a joint model of the within-host viral kinetics of SARS-CoV-2
Bayesian joint model infers infectious virus shedding trajectories and derived infectiousness metrics from PCR and other proxies in SARS-CoV-2 using data from five cohorts of roughly 2000 infections.
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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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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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Quantifying Weighted Morphological Content of Large-Scale Structures via Simulation-Based Inference
Simulation-based inference on Big Sobol Sequence halos at z=0.5 shows CMD+MFs improves σ8 and Ωm precision by ~27% over MFs alone and outperforms PS by ~45% in mass-selected samples at matched scales.
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Gravitational-Wave Parameter Estimation in non-Gaussian noise using Score-Based Likelihood Characterization
Score-based diffusion models learn the empirical distribution of real LIGO noise to enable unbiased gravitational-wave parameter estimation under only an additivity assumption.
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A parameterization of anisotropic Gaussian fields with penalized complexity priors
New smooth invertible parameterization of anisotropic GF correlation length and diffusion matrix, with PC priors that penalize finite range and nonzero anisotropy for constant-parameter models.
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Population synthesis of active galactic nuclei based on the radiation-regulated unification model
Using ray-tracing simulations and simulation-based inference, the authors construct an AGN population that reproduces the cosmic X-ray background, number counts, and absorption properties, deriving an intrinsic Compton-thick fraction of 40±3%.
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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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MIRA: A Score for Conditional Distribution Accuracy and Model Comparison
MIRA is a new analytic score for conditional distribution accuracy derived from equal probability mass assignment, enabling Bayesian model comparison via direct posterior validation.
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Tests of General Relativity with Binary Black Holes from the second LIGO-Virgo Gravitational-Wave Transient Catalog
No evidence for deviations from general relativity is found in LIGO-Virgo binary black hole events, with improved constraints on waveform parameters, graviton mass, and ringdown properties.
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Bayesian Extreme Value Theory with Hawkes-AR-Gumbel Dependence for Extreme CVaR Estimation in Operational Risk
Bayesian EVT with Hawkes-AR-Gumbel dependence estimates CVaR up to 99.995% on simulated operational risk data and outperforms independent and shared-factor baselines.
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Bayesian inference for compact binary coalescences with BILBY: Validation and application to the first LIGO--Virgo gravitational-wave transient catalogue
BILBY is validated on simulated compact binary signals and reproduces the eleven GWTC-1 results with configuration and output files provided for reproduction.
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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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Application of Machine Learning to 21 cm Cosmology
A review chapter groups machine learning methods for 21 cm cosmology by their pipeline roles in handling contaminated data, accelerating simulations, and inferring astrophysical parameters.
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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.