SOTAVerified

Bayesian Inference

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 201–225 of 2226 papers

TitleStatusHype
Bayesian Calibration of Win Rate Estimation with LLM EvaluatorsCode0
Uncertainty Prediction Neural Network (UpNet): Embedding Artificial Neural Network in Bayesian Inversion Framework to Quantify the Uncertainty of Remote Sensing RetrievalCode0
Bayesian Inference in Recurrent Explicit Duration Switching Linear Dynamical Systems—0
Compositional simulation-based inference for time seriesCode0
Modelling Alzheimer's Protein Dynamics: A Data-Driven Integration of Stochastic Methods, Machine Learning and Connectome Insights—0
Stein Variational Newton Neural Network EnsemblesCode0
Improving Trust Estimation in Human-Robot Collaboration Using Beta Reputation at Fine-grained TimescalesCode0
Interacting Large Language Model Agents. Interpretable Models and Social Learning—0
Constrained Sampling with Primal-Dual Langevin Monte CarloCode0
Uncertainty-based Offline Variational Bayesian Reinforcement Learning for Robustness under Diverse Data CorruptionsCode0
Bayesian Approaches for Revealing Complex Neural Network Dynamics in Parkinson's Disease—0
ELBOing Stein: Variational Bayes with Stein Mixture InferenceCode0
Full-waveform earthquake source inversion using simulation-based inferenceCode0
KALAM: toolKit for Automating high-Level synthesis of Analog computing systeMs—0
Variational Bayes Decomposition for Inverse Estimation with Superimposed Multispectral Intensity—0
A Stein Gradient Descent Approach for Doubly Intractable Distributions—0
BSD: a Bayesian framework for parametric models of neural spectraCode2
Embedded Nonlocal Operator Regression (ENOR): Quantifying model error in learning nonlocal operators—0
Dimension reduction via score ratio matching—0
Noise-Aware Differentially Private Variational Inference—0
TRADE: Transfer of Distributions between External Conditions with Normalizing FlowsCode0
FeBiM: Efficient and Compact Bayesian Inference Engine Empowered with Ferroelectric In-Memory Computing—0
Conditional diffusions for amortized neural posterior estimationCode0
Scalable Random Feature Latent Variable ModelsCode1
A Bayesian Perspective on the Maximum Score Problem—0
Show:102550
← PrevPage 9 of 90Next →

Benchmark Results

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