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 391–400 of 2226 papers

TitleStatusHype
Joint Parameter and Parameterization Inference with Uncertainty Quantification through Differentiable Programming—0
Statistical Mechanics of Dynamical System Identification—0
Listening to the Noise: Blind Denoising with Gibbs DiffusionCode1
Sequential transport maps using SoS density estimation and α-divergencesCode0
Demonstration of Robust and Efficient Quantum Property Learning with Shallow ShadowsCode2
Stochastic Approximation with Biased MCMC for Expectation MaximizationCode0
Quasi-Bayesian Estimation and Inference with Control Functions—0
Prediction of the SYM-H Index Using a Bayesian Deep Learning Method with Uncertainty Quantification—0
Pragmatic Instruction Following and Goal Assistance via Cooperative Language-Guided Inverse PlanningCode1
Towards a Digital Twin Framework in Additive Manufacturing: Machine Learning and Bayesian Optimization for Time Series Process Optimization—0
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Benchmark Results

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