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 851–860 of 2226 papers

TitleStatusHype
Flexible and Hierarchical Prior for Bayesian Nonnegative Matrix Factorization—0
Parameters identification for an inverse problem arising from a binary option using a Bayesian inference approach—0
Quasi Black-Box Variational Inference with Natural Gradients for Bayesian Learning—0
RL with KL penalties is better viewed as Bayesian inference—0
Variational Inference for Bayesian Bridge Regression—0
DPER: Dynamic Programming for Exist-Random Stochastic SATCode0
Nonblind image deconvolution via leveraging model uncertainty in an untrained deep neural networkCode0
Marginal and Joint Cross-Entropies & Predictives for Online Bayesian Inference, Active Learning, and Active Sampling—0
DPO: Dynamic-Programming Optimization on Hybrid ConstraintsCode0
Intuitive and Efficient Human-robot Collaboration via Real-time Approximate Bayesian Inference—0
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Benchmark Results

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