A Bayesian global Fréchet regression method is introduced via a Fréchet Bayes rule that reduces the problem to scalar tasks, allows prior-data interpolation, and remains valid under moment conditions using weak conditional expectations.
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Pith papers citing it
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2026 5verdicts
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A Bayesian hierarchical model integrates coherence penalization and level-specific focus into forecasting estimation, yielding improved predictive accuracy on simulated and Australian tourism data.
Rectified AI priors, obtained by correcting AI-induced data laws before embedding them in techniques like Dirichlet process priors, reduce bias, improve credible interval coverage, and boost performance in tasks like skin disease classification.
Constrained weighted Bayesian bootstrap extends weighted Bayesian bootstrap to constrained posteriors with asymptotics matching restricted MLE and is demonstrated on option pricing.
Relative plausibility theory supplies a computational-level account of comparing explanations against evidence in legal proof, while probabilistic methods supply algorithmic-level implementations, and the two correspond when plausibility judgments meet basic coherence conditions.
citing papers explorer
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Bayesian Global Fr\'echet Regression via Weak Conditional Expectations
A Bayesian global Fréchet regression method is introduced via a Fréchet Bayes rule that reduces the problem to scalar tasks, allows prior-data interpolation, and remains valid under moment conditions using weak conditional expectations.
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Hierarchical Bayes meets hierarchical forecasting: A flexible framework for level-focused forecasts
A Bayesian hierarchical model integrates coherence penalization and level-specific focus into forecasting estimation, yielding improved predictive accuracy on simulated and Australian tourism data.
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Supercharging Bayesian Inference with Reliable AI-Informed Priors
Rectified AI priors, obtained by correcting AI-induced data laws before embedding them in techniques like Dirichlet process priors, reduce bias, improve credible interval coverage, and boost performance in tasks like skin disease classification.
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Constrained Weighted Bayesian Bootstrap
Constrained weighted Bayesian bootstrap extends weighted Bayesian bootstrap to constrained posteriors with asymptotics matching restricted MLE and is demonstrated on option pricing.
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Relative plausibility versus probabilism: A level-of-analysis error in juridical proof
Relative plausibility theory supplies a computational-level account of comparing explanations against evidence in legal proof, while probabilistic methods supply algorithmic-level implementations, and the two correspond when plausibility judgments meet basic coherence conditions.