SOTAVerified

Bayesian Inference

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

Papers

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Showing 1321–1330 of 2226 papers

TitleStatusHype
DPGIIL: Dirichlet Process-Deep Generative Model-Integrated Incremental Learning for Clustering in Transmissibility-based Online Structural Anomaly Detection—0
Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data—0
Dynamical System Identification, Model Selection and Model Uncertainty Quantification by Bayesian Inference—0
Sequential Importance Sampling for Hybrid Model Bayesian Inference to Support Bioprocess Mechanism Learning and Robust Control—0
Dynamic Calibration of Nonlinear Sensors with Time-Drifts and Delays by Bayesian Inference—0
Dynamic Likelihood-free Inference via Ratio Estimation (DIRE)—0
DynamicRouteGPT: A Real-Time Multi-Vehicle Dynamic Navigation Framework Based on Large Language Models—0
Dynamics-Adaptive Continual Reinforcement Learning via Progressive Contextualization—0
Dynamics on Lie groups with applications to attitude estimation—0
Efficient acquisition rules for model-based approximate Bayesian computation—0
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Benchmark Results

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