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 411–420 of 2226 papers

TitleStatusHype
Bayesian Uncertainty for Gradient Aggregation in Multi-Task LearningCode1
Diffusive Gibbs SamplingCode1
Recent methods from statistical inference and machine learning to improve integrative modeling of macromolecular assemblies—0
Enhancing Gaussian Process Surrogates for Optimization and Posterior Approximation via Random Exploration—0
Recovering Mental Representations from Large Language Models with Markov Chain Monte Carlo—0
Dynamical System Identification, Model Selection and Model Uncertainty Quantification by Bayesian Inference—0
Incoherent Probability Judgments in Large Language Models—0
Distributed Markov Chain Monte Carlo Sampling based on the Alternating Direction Method of MultipliersCode0
Bayesian Inference Accelerator for Spiking Neural Networks—0
Particle-MALA and Particle-mGRAD: Gradient-based MCMC methods for high-dimensional state-space modelsCode1
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Benchmark Results

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