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 331–340 of 2226 papers

TitleStatusHype
Bayesian Inference with Deep Weakly Nonlinear Networks—0
A hierarchical Bayesian model for syntactic priming—0
Towards Better Understanding of In-Context Learning Ability from In-Context Uncertainty Quantification—0
Fast Bayesian Inference for Neutrino Non-Standard Interactions at Dark Matter Direct Detection ExperimentsCode0
Poisson Variational AutoencoderCode1
Gaussian Measures Conditioned on Nonlinear Observations: Consistency, MAP Estimators, and Simulation—0
The future of cosmological likelihood-based inference: accelerated high-dimensional parameter estimation and model comparisonCode2
Evaluating and Modeling Social Intelligence: A Comparative Study of Human and AI CapabilitiesCode0
Accelerating Multilevel Markov Chain Monte Carlo Using Machine Learning Models—0
Estimating Idea Production: A Methodological SurveyCode0
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Benchmark Results

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