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 41–50 of 2226 papers

TitleStatusHype
Generalized Power Priors for Improved Bayesian Inference with Historical Data—0
Inter-Subject Variance Transfer Learning for EMG Pattern Classification Based on Bayesian Inference—0
Personalized Bayesian Federated Learning with Wasserstein Barycenter Aggregation—0
Compositional amortized inference for large-scale hierarchical Bayesian models—0
Understanding Task Representations in Neural Networks via Bayesian Ablation—0
SNAPE-PM: Building and Utilizing Dynamic Partner Models for Adaptive Explanation GenerationCode0
Variational Bayesian Inference for Time-Varying Massive MIMO Channels: Estimation and Detection—0
Efficient MCMC Sampling with Expensive-to-Compute and Irregular Likelihoods—0
Uncertainty-Aware Surrogate-based Amortized Bayesian Inference for Computationally Expensive Models—0
Bayesian Estimation of Causal Effects Using Proxies of a Latent Interference NetworkCode0
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Benchmark Results

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