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 181–190 of 2226 papers

TitleStatusHype
Path-Guided Particle-based SamplingCode0
MACAW: A Causal Generative Model for Medical ImagingCode0
Physics-informed Gaussian Processes as Linear Model Predictive Controller—0
Extension of compressive sampling to binary vector recovery for model-based defect imaging—0
Investigating Plausibility of Biologically Inspired Bayesian Learning in ANNs—0
ESS-ReduNet: Enhancing Subspace Separability of ReduNet via Dynamic Expansion with Bayesian Inference—0
Belief patterns with information processing—0
AutoElicit: Using Large Language Models for Expert Prior Elicitation in Predictive ModellingCode0
sbi reloaded: a toolkit for simulation-based inference workflowsCode4
Decision Making under the Exponential Family: Distributionally Robust Optimisation with Bayesian Ambiguity Sets—0
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Benchmark Results

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