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 1461–1470 of 2226 papers

TitleStatusHype
Improved algorithm for neuronal ensemble inference by Monte Carlo method—0
A Bayesian/Information Theoretic Model of Bias Learning—0
Streaming Bayesian Inference for Crowdsourced Classification—0
Combining human cell line transcriptome analysis and Bayesian inference to build trustworthy machine learning models for prediction of animal toxicity in drug development—0
Not All Claims are Created Equal: Choosing the Right Statistical Approach to Assess HypothesesCode0
Machine Learning using the Variational Predictive Information Bottleneck with a Validation Set—0
Variational Bayesian inference of hidden stochastic processes with unknown parameters—0
Phase transitions and optimal algorithms for semi-supervised classifications on graphs: from belief propagation to graph convolution network—0
Continual Multi-task Gaussian ProcessesCode0
Bayesian causal inference via probabilistic program synthesis—0
Show:102550
← PrevPage 147 of 223Next →

Benchmark Results

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