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 961–970 of 2226 papers

TitleStatusHype
Fast Bayesian Inference for Gaussian Cox Processes via Path Integral Formulation—0
Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression—0
Bayesian Inference for Left-Truncated Log-Logistic Distributions for Time-to-event Data Analysis—0
An Overview of Uncertainty Quantification Methods for Infinite Neural Networks—0
Fast Convergence for Langevin Diffusion with Manifold Structure—0
Fast Convergence for Langevin with Matrix Manifold Structure—0
A deep surrogate approach to efficient Bayesian inversion in PDE and integral equation models—0
Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes—0
Sampling from Log-Concave Distributions over Polytopes via a Soft-Threshold Dikin Walk—0
A Bayesian Long Short-Term Memory Model for Value at Risk and Expected Shortfall Joint Forecasting—0
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Benchmark Results

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