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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 1111–1120 of 2226 papers

TitleStatusHype
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks—0
The linear conditional expectation in Hilbert space—0
Theoretical and Computational Guarantees of Mean Field Variational Inference for Community Detection—0
Theory of Minds: Understanding Behavior in Groups Through Inverse Planning—0
The Population Posterior and Bayesian Modeling on Streams—0
The principles of adaptation in organisms and machines II: Thermodynamics of the Bayesian brain—0
The Recycling Gibbs Sampler for Efficient Learning—0
Thermodynamic Bayesian Inference—0
The Stochastic complexity of spin models: Are pairwise models really simple?—0
The Variational Bayesian Inference for Network Autoregression Models—0
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Benchmark Results

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