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 901–910 of 2226 papers

TitleStatusHype
Bayesian Inference for Radar Imagery Based Surveillance—0
A partial likelihood approach to tree-based density modeling and its application in Bayesian inference—0
Dynamics-Adaptive Continual Reinforcement Learning via Progressive Contextualization—0
Entropy-regularized Gradient Estimators for Approximate Bayesian Inference—0
DynamicRouteGPT: A Real-Time Multi-Vehicle Dynamic Navigation Framework Based on Large Language Models—0
Epidemic mitigation by statistical inference from contact tracing data—0
Dynamic Likelihood-free Inference via Ratio Estimation (DIRE)—0
Episodic memory for continual model learning—0
Data Augementation with Polya Inverse Gamma—0
A Parameter-Free Learning Automaton Scheme—0
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Benchmark Results

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