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 851–860 of 2226 papers

TitleStatusHype
Approximate Gibbs Sampler for Efficient Inference of Hierarchical Bayesian Models for Grouped Count Data—0
Looking at the posterior: accuracy and uncertainty of neural-network predictions—0
Bayesian Learning for Neural Networks: an algorithmic survey—0
Few-shot Non-line-of-sight Imaging with Signal-surface Collaborative Regularization—0
Active Exploration based on Information Gain by Particle Filter for Efficient Spatial Concept Formation—0
Monitoring machine learning (ML)-based risk prediction algorithms in the presence of confounding medical interventionsCode0
Orthogonal Polynomials Approximation Algorithm (OPAA):a functional analytic approach to estimating probability densities—0
Understanding Approximation for Bayesian Inference in Neural Networks—0
Bayesian score calibration for approximate modelsCode0
Generalization of generative model for neuronal ensemble inference method—0
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Benchmark Results

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