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 1326–1350 of 2226 papers

TitleStatusHype
Dynamic Likelihood-free Inference via Ratio Estimation (DIRE)—0
DynamicRouteGPT: A Real-Time Multi-Vehicle Dynamic Navigation Framework Based on Large Language Models—0
Dynamics-Adaptive Continual Reinforcement Learning via Progressive Contextualization—0
Dynamics on Lie groups with applications to attitude estimation—0
Efficient acquisition rules for model-based approximate Bayesian computation—0
Efficient Approximate Inference with Walsh-Hadamard Variational Inference—0
Efficient Attack Graph Analysis through Approximate Inference—0
Efficient Bayesian Computation Using Plug-and-Play Priors for Poisson Inverse Problems—0
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
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Benchmark Results

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