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 76–100 of 2226 papers

TitleStatusHype
Advancing Autonomous Vehicle Safety: A Combined Fault Tree Analysis and Bayesian Network Approach—0
Programs as Singularities—0
Stacking Variational Bayesian Monte Carlo—0
The challenge of uncertainty quantification of large language models in medicine—0
Probabilistic Position-Aided Beam Selection for mmWave MIMO Systems—0
Constrained Gaussian Process Motion Planning via Stein Variational Newton Inference—0
Incorporating the ChEES Criterion into Sequential Monte Carlo Samplers—0
Multi-fidelity Parameter Estimation Using Conditional Diffusion Models—0
Barrier Certificates for Unknown Systems with Latent States and Polynomial Dynamics using Bayesian Inference—0
HomeEmergency -- Using Audio to Find and Respond to Emergencies in the Home—0
Bayesian Predictive Coding—0
Accelerated Stein Variational Gradient FlowCode0
A friendly introduction to triangular transportCode1
DeepRV: pre-trained spatial priors for accelerated disease mapping—0
Enhancing Graphical Lasso: A Robust Scheme for Non-Stationary Mean Data—0
E-PINNs: Epistemic Physics-Informed Neural Networks—0
AutoBayes: A Compositional Framework for Generalized Variational Inference—0
Bayesian Teaching Enables Probabilistic Reasoning in Large Language Models—0
SuperARC: An Agnostic Test for Narrow, General, and Super Intelligence Based On the Principles of Recursive Compression and Algorithmic ProbabilityCode0
Efficient Bayesian Computation Using Plug-and-Play Priors for Poisson Inverse Problems—0
FedBEns: One-Shot Federated Learning based on Bayesian Ensemble—0
Simulation-based Bayesian inference under model misspecification—0
Entropy-regularized Gradient Estimators for Approximate Bayesian Inference—0
Understanding the Trade-offs in Accuracy and Uncertainty Quantification: Architecture and Inference Choices in Bayesian Neural NetworksCode0
Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model—0
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Benchmark Results

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