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 731–740 of 2226 papers

TitleStatusHype
SoftBart: Soft Bayesian Additive Regression Trees—0
Bayesian Inference of Transition Matrices from Incomplete Graph Data with a Topological Prior—0
Variational Bayesian Inference Clustering Based Joint User Activity and Data Detection for Grant-Free Random Access in mMTC—0
Bayesian Methods in Automated Vehicle's Car-following Uncertainties: Enabling Strategic Decision Making—0
Learning Latent Structural Causal Models—0
GFlowOut: Dropout with Generative Flow Networks—0
Bayesian inference is facilitated by modular neural networks with different time scalesCode0
Uncertain Evidence in Probabilistic Models and Stochastic Simulators—0
Efficient identification of informative features in simulation-based inferenceCode0
Bayesian deep learning framework for uncertainty quantification in high dimensions—0
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Benchmark Results

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