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 401–425 of 2226 papers

TitleStatusHype
DART: Depth-Enhanced Accurate and Real-Time Background Matting—0
Accelerating Convergence of Stein Variational Gradient Descent via Deep UnfoldingCode0
Regularization by denoising: Bayesian model and Langevin-within-split Gibbs sampling—0
BlackJAX: Composable Bayesian inference in JAXCode5
Human Goal Recognition as Bayesian Inference: Investigating the Impact of Actions, Timing, and Goal Solvability—0
Semi-parametric financial risk forecasting incorporating multiple realized measures—0
Improvement and generalization of ABCD method with Bayesian inference—0
Bayesian Deep Learning Via Expectation Maximization and Turbo Deep Approximate Message Passing—0
A Factor Graph Model of Trust for a Collaborative Multi-Agent System—0
SMC Is All You Need: Parallel Strong Scaling—0
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
Dynamical System Identification, Model Selection and Model Uncertainty Quantification by Bayesian Inference—0
Incoherent Probability Judgments in Large Language Models—0
Recovering Mental Representations from Large Language Models with Markov Chain Monte Carlo—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
A hybrid tau-leap for simulating chemical kinetics with applications to parameter estimation—0
Quantifying cell cycle regulation by tissue crowdingCode0
Reliability Analysis of Complex Systems using Subset Simulations with Hamiltonian Neural Networks—0
Stable generative modeling using Schrödinger bridges—0
Neural Population Decoding and Imbalanced Multi-Omic Datasets For Cancer Subtype Diagnosis—0
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Benchmark Results

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