Prequential posteriors

arXiv:2511.17721 · stat.ML, cs.LG · Submitted 2025-11-21 · Read on arXiv

stat.ML, cs.LG

Submitted: 2025-11-21

Updated: 2026-08-28

Terminology

• Calibration — 23× in this paper · explained in 7 episodes
• Consistency — 23× in this paper · explained in 3 episodes
• Loss Function — 18× in this paper · explained in 2 episodes
• Accuracy — 11× in this paper · explained in 1 episode
• Generative Model — 9× in this paper · explained in 2 episodes
• Sequential Monte Carlo — 8× in this paper
• Generative Models — 7× in this paper · explained in 2 episodes
• Probabilistic Forecasting — 6× in this paper · explained in 3 episodes
• Reinforcement Learning — 5× in this paper · explained in 4 episodes
• Bayesian Inference — 4× in this paper · explained in 5 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Degeneracy — 3× in this paper · explained in 3 episodes
• Online Reinforcement Learning — 3× in this paper · explained in 2 episodes
• Sequential Monte Carlo (SMC) — 3× in this paper · explained in 2 episodes
• Digital Twin — 2× in this paper · explained in 4 episodes
• Langevin Dynamics — 2× in this paper · explained in 2 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Adversarial Training — 1× in this paper · explained in 1 episode
• Bayesian Framework — 1× in this paper · explained in 3 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Curse of Dimensionality — 1× in this paper · explained in 6 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Fisher Information — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Root Mean-Square Error — 1× in this paper
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes
• U-net architecture — 1× in this paper · explained in 3 episodes
• Uncertainty Quantification — 1× in this paper · explained in 8 episodes
• Wasserstein Distance — 1× in this paper · explained in 4 episodes

Sources

• On Learnability under General Stochastic Processes
• Outlier-robust Kalman Filtering through Generalised Bayes
• Likelihood-free inference by ratio estimation
• AIFS-CRPS: Ensemble forecasting using a model trained with a loss function based on the Continuous Ranked Probability Score
• FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators
• GenCast: Diffusion-based ensemble forecasting for medium-range weather
• Generalized Bayesian deep reinforcement learning
• Proper scoring rules for estimation and forecast evaluation

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