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 101–110 of 2226 papers

TitleStatusHype
Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaicsCode1
Encoding the latent posterior of Bayesian Neural Networks for uncertainty quantificationCode1
A Bayesian algorithm for retrosynthesisCode1
Exact Bayesian Inference on Discrete Models via Probability Generating Functions: A Probabilistic Programming ApproachCode1
Fast and Accurate Forecasting of COVID-19 Deaths Using the SIkJα ModelCode1
Fast and robust Bayesian Inference using Gaussian Processes with GPryCode1
A Practical Introduction to Bayesian Estimation of Causal Effects: Parametric and Nonparametric ApproachesCode1
Flexible Bayesian Nonlinear Model ConfigurationCode1
A Bayesian approach for extracting free energy profiles from cryo-electron microscopy experiments using a path collective variableCode1
A practical tutorial on Variational BayesCode1
Show:102550
← PrevPage 11 of 223Next →

Benchmark Results

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