arXiv Machine Learning
Jul 27
Simulation-Based Empirical Bayes
arXiv:2607. 21843v1 Announce Type: cross Abstract: Empirical Bayes (EB) performs simultaneous inference across many related latent variables.
Amortized ratio-estimation importance sampling and localized simulation-based calibration for intractable likelihoods
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arXiv AI
Jun 26
Simulation-based inference for rapid Bayesian parameter estimation in epidemiological models: a comparison with MCMC
arXiv:2606. 27286v1 Announce Type: new Abstract: Mechanistic epidemiological models are widely used to support infectious disease forecasting and public-health decision making.
arXiv Machine Learning
Jun 11
CP4SBI: Local Conformal Calibration of Credible Sets in Simulation-Based Inference
arXiv:2508. 17077v3 Announce Type: replace-cross Abstract: Current experimental scientists have been increasingly relying on simulation-based inference (SBI) to invert complex non-linear models with intractable likelihoods.
arXiv Statistics ML
Aug 25
Guidance for Prior Change via Density Ratio Estimation
arXiv:2608.21729v1 Announce Type: new Abstract: Simulation-Based Inference (SBI) serves as a vital framework for parameter inference in scientific fields where simulators involve intractable likeliho...
arXiv Machine Learning
Jul 9
Bifidelity Parameter Estimation Using Conditional Diffusion Models
arXiv:2504. 01894v2 Announce Type: replace Abstract: We present a bifidelity method for uncertainty quantification of parameter estimates in complex systems, leveraging generative models trained to sample the target conditional distribution.
arXiv Machine Learning
Jun 4
Flow Matching Calibration for Simulation-Based Inference under Model Misspecification
arXiv:2509. 23385v5 Announce Type: replace-cross Abstract: Simulation-based inference (SBI) is transforming experimental sciences by enabling parameter estimation in complex non-linear models from simulated data.
arXiv Machine Learning
Jun 19
Variational Consensus Monte Carlo for Bayesian Mixture
arXiv:2606. 19643v1 Announce Type: cross Abstract: Motivated by the privacy, sensitivity and sharing limitations of health data, we present a comprehensive pipeline for inference of Bayesian mixture models within a federated learning setting, i.
arXiv Statistics ML
Sep 3
Robust Bayesian Inference for Unnormalized Models with Mixed-Domain Data
The paper introduces SME-BETEL, a semiparametric Bayesian method that merges score matching estimating equations with Bayesian exponentially tilted empirical likelihood to perform inference on models with intractable normalizing constants. SME-BETEL avoids evaluating these constants and eliminates the need for learning-rate calibration, while providing consistency, asymptotic normality, and a Bernstein‑von Mises theorem that guarantees asymptotically calibrated credible sets even under model misspecification. The authors extend the framework to mixed‑domain data, enabling robust inference for doubly‑intractable models such as spatial preferential sampling, and demonstrate its effectiveness through simulations and an ozone‑monitoring application.
arXiv Machine Learning
Jun 24
Predictive variational inference: Learn the predictively optimal posterior distribution
arXiv:2410. 14843v4 Announce Type: replace-cross Abstract: Vanilla variational inference finds an optimal approximation to the Bayesian posterior distribution, but even the exact Bayesian posterior is often not meaningful under model misspecification.
arXiv Statistics ML
Aug 28
A Two-step Metropolis Hastings Method for Bayesian Empirical Likelihood Computation with Application to Quantile Regression and Bayesian Model Selection
The paper introduces a two-step Metropolis–Hastings algorithm designed to efficiently sample from Bayesian empirical likelihood (BayesEL) posterior distributions, addressing challenges posed by the complex, often non‑convex support of empirical likelihood. The method leverages current parameter values and estimating equations to propose new values for remaining parameters, making it suitable for problems with discontinuous estimating equations such as simultaneous quantile regression. Additionally, the approach extends naturally to BayesEL model selection via reversible‑jump MCMC, and the authors demonstrate its utility through several real‑life applications.
arXiv Machine Learning
Jul 14
Hierarchical Bayesian Quadrature
arXiv:2607. 10793v1 Announce Type: new Abstract: Numerical integration is a cornerstone of various scientific computing applications, such as engineering simulations and model evidence computations in probabilistic machine learning.
arXiv Statistics ML
Sep 22
A Three-Way Testing Framework for Quantifying Epistemic Calibration Uncertainty in SBI
arXiv:2609.24419v1 Announce Type: cross Abstract: Current experimental scientists increasingly rely on simulation-based inference (SBI) to invert complex models with intractable likelihoods. A primar...