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 2076–2100 of 2226 papers

TitleStatusHype
Efficient Bayesian Inference for a Gaussian Process Density Model—0
Efficient Bayesian Inference for Nested Simulators—0
Efficient Bayesian inference using physics-informed invertible neural networks for inverse problems—0
Efficient Bayesian species tree inference under the multi-species coalescent—0
Efficient Bayesian synthetic likelihood with whitening transformations—0
Efficient Debiased Evidence Estimation by Multilevel Monte Carlo Sampling—0
Efficient Likelihood Bayesian Constrained Local Model—0
Efficient Low-Order Approximation of First-Passage Time Distributions—0
Efficient MCMC Sampling with Expensive-to-Compute and Irregular Likelihoods—0
Efficient Membership Inference Attacks by Bayesian Neural Network—0
Efficient Online Inference and Learning in Partially Known Nonlinear State-Space Models by Learning Expressive Degrees of Freedom Offline—0
Efficient posterior inference & generalization in physics-based Bayesian inference with conditional GANs—0
Efficient Reinforcement Learning with Large Language Model Priors—0
Efficient Sound Field Reconstruction with Conditional Invertible Neural Networks—0
Efficient transfer learning and online adaptation with latent variable models for continuous control—0
Efficient Weight-Space Laplace-Gaussian Filtering and Smoothing for Sequential Deep Learning—0
EinSteinVI: General and Integrated Stein Variational Inference—0
Elements of Sequential Monte Carlo—0
Eliciting Language Model Behaviors with Investigator Agents—0
Embarrassingly parallel MCMC using deep invertible transformations—0
Embedded Nonlocal Operator Regression (ENOR): Quantifying model error in learning nonlocal operators—0
EMG Pattern Recognition via Bayesian Inference with Scale Mixture-Based Stochastic Generative Models—0
EMHMM Simulation Study—0
Enhanced gradient-based MCMC in discrete spaces—0
Enhancing Graphical Lasso: A Robust Scheme for Non-Stationary Mean Data—0
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Benchmark Results

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