SOURCE-LINKED INTELLIGENCE

Robust Bayesian Inference for Unnormalized Models with Mixed-Domain Data

arXiv · AI, language, vision and robotics · article · Sep 1, 2026 · UTC

Many statistical models involve parameter-dependent normalizing constants that are computationally intractable, creating substantial obstacles to standard Bayesian inference. Although existing likelihood-based algorithms can often circumvent these constants, their uncertainty quantification may be poorly calibrated under model misspecification. To address these challenges, we propose SME-BETEL, a semiparametric Bayesian framework that combines score matching estimating equations with Bayesian exponentially tilted empirical likelihood. The resulting posterior avoids evaluation of normalizing co

Read original source ↗ Open in workspace

recordType

paper

region

Global