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 431–440 of 2226 papers

TitleStatusHype
Bayesian Analysis of Combinatorial Gaussian Process Bandits—0
Diffusion Models With Learned Adaptive NoiseCode1
Stochastic Control Barrier Functions with Bayesian Inference for Unknown Stochastic Differential Equations—0
Distributed Bayesian Inference for Large-Scale IoT Systems—0
Observation-Augmented Contextual Multi-Armed Bandits for Robotic Search and Exploration—0
Gaussian process learning of nonlinear dynamicsCode1
Safeguarded Progress in Reinforcement Learning: Safe Bayesian Exploration for Control Policy Synthesis—0
Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear ModelCode0
Do Bayesian Neural Networks Improve Weapon System Predictive Maintenance?—0
Automatic Rao-Blackwellization for Sequential Monte Carlo with Belief PropagationCode1
Show:102550
← PrevPage 44 of 223Next →

Benchmark Results

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