A generative-model-based test for equality of conditional distributions that uses cross-generation, an RKHS-indexed supremum statistic, and multiplier bootstrap, with claimed double robustness to generator errors.
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OASIS performs observation-aware simulation-based inference by reweighting prior samples according to an MMD loss between empirical distributions of observed data and forward-simulated observations, with claimed Monte Carlo consistency and posterior concentration guarantees.
The paper introduces two general frameworks for conditional two-sample testing by converting conditional independence tests or using density ratio estimation to enable marginal comparisons.
citing papers explorer
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Testing Equality of Conditional Distributions via Generative Models
A generative-model-based test for equality of conditional distributions that uses cross-generation, an RKHS-indexed supremum statistic, and multiplier bootstrap, with claimed double robustness to generator errors.
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OASIS: Observation-Aware Simulation-Based Inference via Distributional Matching
OASIS performs observation-aware simulation-based inference by reweighting prior samples according to an MMD loss between empirical distributions of observed data and forward-simulated observations, with claimed Monte Carlo consistency and posterior concentration guarantees.
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General Frameworks for Conditional Two-Sample Testing
The paper introduces two general frameworks for conditional two-sample testing by converting conditional independence tests or using density ratio estimation to enable marginal comparisons.