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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 1261–1270 of 2226 papers

TitleStatusHype
Human Inference in Changing Environments With Temporal Structure—0
Bayesian Inference ForgettingCode0
Probabilistic Inference for Learning from Untrusted Sources—0
On the relationship between a Gamma distributed precision parameter and the associated standard deviation in the context of Bayesian parameter inferenceCode0
Scaling Up Bayesian Uncertainty Quantification for Inverse Problems using Deep Neural Networks—0
Maximum a Posteriori Inference of Random Dot Product Graphs via Conic Programming—0
Control-Data Separation and Logical Condition Propagation for Efficient Inference on Probabilistic Programs—0
Learning optimal Bayesian prior probabilities from data—0
Uncertainty Calibration Error: A New Metric for Multi-Class Classification—0
Bayesian Neural Networks with Variance Propagation for Uncertainty Evaluation—0
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Benchmark Results

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