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 911–920 of 2226 papers

TitleStatusHype
IBIA: Bayesian Inference via Incremental Build-Infer-Approximate operations on Clique Trees—0
Parallel MCMC Without Embarrassing FailuresCode0
Non-Volatile Memory Accelerated Posterior Estimation—0
A Molecular Prior Distribution for Bayesian Inference Based on Wilson StatisticsCode0
Hybridizing Physical and Data-driven Prediction Methods for Physicochemical Properties—0
Fast online inference for nonlinear contextual bandit based on Generative Adversarial Network—0
Learning to Generalize across Domains on Single Test SamplesCode1
The efficacy and generalizability of conditional GANs for posterior inference in physics-based inverse problems—0
Sequential Bayesian experimental designs via reinforcement learning—0
Robust Bayesian Inference for Simulator-based Models via the MMD Posterior BootstrapCode0
Show:102550
← PrevPage 92 of 223Next →

Benchmark Results

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