IPW is justified in post-Bayesian inference by reweighting the KL divergence to produce generalized belief posteriors that correct for selection bias.
Robust generalised
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
stat.ME 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
A general Bayesian framework encodes explicit confidence in uncertainty sources to enable new regularization control and sparsity induction in statistical models.
Predictive Bayesian inference posteriors concentrate onto a forward-model-dependent quantity and produce miscalibrated credible sets unless the predictive model contains the true data-generating process.
citing papers explorer
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Inverse Probability Weighting in a Post-Bayesian World
IPW is justified in post-Bayesian inference by reweighting the KL divergence to produce generalized belief posteriors that correct for selection bias.
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Bayesian inference with sources of uncertainty: from confidence modelling to sparse estimation
A general Bayesian framework encodes explicit confidence in uncertainty sources to enable new regularization control and sparsity induction in statistical models.
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Concentration and Calibration in Predictive Bayesian Inference
Predictive Bayesian inference posteriors concentrate onto a forward-model-dependent quantity and produce miscalibrated credible sets unless the predictive model contains the true data-generating process.