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 2021–2030 of 2226 papers

TitleStatusHype
Tractable Fully Bayesian Inference via Convex Optimization and Optimal Transport Theory—0
Unbiased Bayesian Inference for Population Markov Jump Processes via Random TruncationsCode0
A Simulated Annealing Approach to Bayesian Inference—0
Modeling sequences and temporal networks with dynamic community structures—0
Bayesian inference for spatio-temporal spike-and-slab priors—0
Learning without Recall by Random Walks on Directed Graphs—0
Bayesian Masking: Sparse Bayesian Estimation with Weaker Shrinkage Bias—0
Varying-coefficient models with isotropic Gaussian process priors—0
Zero-Truncated Poisson Tensor Factorization for Massive Binary Tensors—0
Towards Machine Wald—0
Show:102550
← PrevPage 203 of 223Next →

Benchmark Results

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