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Bayesian Inference

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

Papers

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Showing 141–150 of 2226 papers

TitleStatusHype
Beyond Prior Limits: Addressing Distribution Misalignment in Particle Filtering—0
Can Transformers Learn Full Bayesian Inference in Context?Code1
Robust Amortized Bayesian Inference with Self-Consistency Losses on Unlabeled Data—0
Attention-Driven Hierarchical Reinforcement Learning with Particle Filtering for Source Localization in Dynamic Fields—0
Uncertainty Quantification With Noise Injection in Neural Networks: A Bayesian Perspective—0
Can Bayesian Neural Networks Make Confident Predictions?—0
Explainable Lane Change Prediction for Near-Crash Scenarios Using Knowledge Graph Embeddings and Retrieval Augmented Generation—0
Mean and Variance Estimation Complexity in Arbitrary Distributions via Wasserstein Minimization—0
FLORA: Formal Language Model Enables Robust Training-free Zero-shot Object Referring Analysis—0
A recursive Bayesian neural network for constitutive modeling of sands under monotonic loading—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1F-SWAAccuracy83.61—Unverified
2F-SWAGAccuracy80.93—Unverified