SOTAVerified

Bayesian Inference

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

Papers

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Showing 1981–1990 of 2226 papers

TitleStatusHype
High-dimensional Bayesian inference via the Unadjusted Langevin Algorithm—0
Fast Rates for General Unbounded Loss Functions: from ERM to Generalized Bayes—0
Bayesian Inference of Recursive Sequences of Group Activities from Tracks—0
Variational Bayesian Inference of Line Spectra—0
A Formal Calculus for International Relations Computation and Evaluation—0
Bayesian inference in hierarchical models by combining independent posteriors—0
GPU Computing in Bayesian Inference of Realized Stochastic Volatility Model—0
Exact Bayesian inference for off-line change-point detection in tree-structured graphical models—0
On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis—0
Accelerating a hybrid continuum-atomistic fluidic model with on-the-fly machine learning—0
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Benchmark Results

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