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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 2141–2150 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
Fast Burst-Sparsity Learning Approach for Massive MIMO-OTFS Channel Estimation—0
Fast Convergence for Langevin Diffusion with Manifold Structure—0
Fast Convergence for Langevin with Matrix Manifold Structure—0
Fast Dual Variational Inference for Non-Conjugate LGMs—0
Sampling from Log-Concave Distributions over Polytopes via a Soft-Threshold Dikin Walk—0
Faster Uncertainty Quantification for Inverse Problems with Conditional Normalizing Flows—0
Fast Low-Rank Bayesian Matrix Completion with Hierarchical Gaussian Prior Models—0
Fast online inference for nonlinear contextual bandit based on Generative Adversarial Network—0
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Benchmark Results

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