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Bayesian Inference

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

Papers

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Showing 511–520 of 2226 papers

TitleStatusHype
Bounded rationality in structured density estimation—0
Data-driven Modeling and Inference for Bayesian Gaussian Process ODEs via Double Normalizing FlowsCode0
MFRL-BI: Design of a Model-free Reinforcement Learning Process Control Scheme by Using Bayesian Inference—0
Physics-informed Bayesian inference of external potentials in classical density-functional theory—0
Learning Minimalistic Tsetlin Machine Clauses with Markov Boundary-Guided PruningCode1
Reducing the False Positive Rate Using Bayesian Inference in Autonomous Driving Perception—0
Affine Invariant Ensemble Transform Methods to Improve Predictive Uncertainty in Neural Networks—0
A Probabilistic Semi-Supervised Approach with Triplet Markov Chains—0
Signatures of Bayesian inference emerge from energy efficient synapsesCode0
Learning Active Subspaces for Effective and Scalable Uncertainty Quantification in Deep Neural Networks—0
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Benchmark Results

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