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 1211–1220 of 2226 papers

TitleStatusHype
Scalable Quasi-Bayesian Inference for Instrumental Variable Regression—0
Scalable Variational Inference in Log-supermodular Models—0
Scalable Vertical Federated Learning via Data Augmentation and Amortized Inference—0
Beyond the Power Law: Estimation, Goodness-of-Fit, and a Semiparametric Extension in Complex Networks—0
Scale invariant process regression: Towards Bayesian ML with minimal assumptions—0
Scaling Bayesian inference of mixed multinomial logit models to very large datasets—0
Scaling multi-species occupancy models to large citizen science datasets—0
Scaling Nonparametric Bayesian Inference via Subsample-Annealing—0
Scaling Up Bayesian Uncertainty Quantification for Inverse Problems using Deep Neural Networks—0
Seeing Tree Structure from Vibration—0
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Benchmark Results

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