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 631–640 of 2226 papers

TitleStatusHype
Consistent Online Gaussian Process Regression Without the Sample Complexity Bottleneck—0
Can You Trust This Prediction? Auditing Pointwise Reliability After Learning—0
Cascaded Calibration of Mechatronic Systems via Bayesian Inference—0
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models—0
Causal Inference through a Witness Protection Program—0
Predictive Coding beyond Correlations—0
Challenges and Opportunities in Approximate Bayesian Deep Learning for Intelligent IoT Systems—0
Active inference and deep generative modeling for cognitive ultrasound—0
Constrained Gaussian Process Motion Planning via Stein Variational Newton Inference—0
Augmented Message Passing Stein Variational Gradient Descent—0
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Benchmark Results

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