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 1671–1680 of 2226 papers

TitleStatusHype
Mathematical modelling, selection and hierarchical inference to determine the minimal dose in IFNα therapy against Myeloproliferative Neoplasms—0
Max-Margin Majority Voting for Learning from Crowds—0
MC^2RAM: Markov Chain Monte Carlo Sampling in SRAM for Fast Bayesian Inference—0
MC-CIM: Compute-in-Memory with Monte-Carlo Dropouts for Bayesian Edge Intelligence—0
MCMC-driven importance samplers—0
Mean and Variance Estimation Complexity in Arbitrary Distributions via Wasserstein Minimization—0
Mean Field for the Stochastic Blockmodel: Optimization Landscape and Convergence Issues—0
Mean Field Network based Graph Refinement with application to Airway Tree Extraction—0
Mean-field theory of graph neural networks in graph partitioning—0
Mean field variational Bayesian inference for support vector machine classification—0
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Benchmark Results

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