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 791–800 of 2226 papers

TitleStatusHype
Scale invariant process regression: Towards Bayesian ML with minimal assumptions—0
Bayesian Inference with Latent Hamiltonian Neural NetworksCode1
A Novel Resource Allocation for Anti-jamming in Cognitive-UAVs: an Active Inference Approach—0
SwISS: A Scalable Markov chain Monte Carlo Divide-and-Conquer Strategy—0
Deep Maxout Network Gaussian Process—0
Optimal Rates for Regularized Conditional Mean Embedding Learning—0
Enhanced gradient-based MCMC in discrete spaces—0
Reliable amortized variational inference with physics-based latent distribution correctionCode1
Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference PerspectiveCode0
Statistical and Computational Trade-offs in Variational Inference: A Case Study in Inferential Model Selection—0
Show:102550
← PrevPage 80 of 223Next →

Benchmark Results

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