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 901–910 of 2226 papers

TitleStatusHype
Approximate Gibbs Sampler for Efficient Inference of Hierarchical Bayesian Models for Grouped Count Data—0
Entropic Matching for Expectation Propagation of Markov Jump Processes—0
Entropy-based Training Methods for Scalable Neural Implicit Sampler—0
Entropy-regularized Gradient Estimators for Approximate Bayesian Inference—0
Bayesian inference of natural selection from allele frequency time series—0
Epidemic mitigation by statistical inference from contact tracing data—0
Feature Selection via the Intervened Interpolative Decomposition and its Application in Diversifying Quantitative Strategies—0
Episodic memory for continual model learning—0
Belief functions induced by random fuzzy sets: A general framework for representing uncertain and fuzzy evidence—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