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.
Assigning a value to a power likelihood in a general Bayesian model,
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
A Bayesian hierarchical model integrates coherence penalization and level-specific focus into forecasting estimation, yielding improved predictive accuracy on simulated and Australian tourism data.
Self-supervised hybrid adaptive Kalman filter learns structured corrections for data-efficient joint tracking and classification.
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
-
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.
-
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.
-
Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification
Self-supervised hybrid adaptive Kalman filter learns structured corrections for data-efficient joint tracking and classification.
-
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.