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 1981–1990 of 2226 papers

TitleStatusHype
Exact Bayesian inference for off-line change-point detection in tree-structured graphical models—0
On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis—0
Accelerating a hybrid continuum-atomistic fluidic model with on-the-fly machine learning—0
Online but Accurate Inference for Latent Variable Models with Local Gibbs Sampling—0
Bayesian Learning of Kernel Embeddings—0
Modeling cumulative biological phenomena with Suppes-Bayes Causal Networks—0
Patterns of Scalable Bayesian Inference—0
A Mathematical Trust Algebra for International Nation Relations Computation and Evaluation—0
Variational Inference for Sparse and Undirected Models—0
A Tractable Fully Bayesian Method for the Stochastic Block Model—0
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Benchmark Results

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