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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 671–680 of 2226 papers

TitleStatusHype
A Mean Field Approach to Empirical Bayes Estimation in High-dimensional Linear Regression—0
Entropic Matching for Expectation Propagation of Markov Jump Processes—0
A closer look at parameter identifiability, model selection and handling of censored data with Bayesian Inference in mathematical models of tumour growth—0
Neural Operator Variational Inference based on Regularized Stein Discrepancy for Deep Gaussian ProcessesCode0
Bounded rationality in structured density estimation—0
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
Affine Invariant Ensemble Transform Methods to Improve Predictive Uncertainty in Neural Networks—0
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Benchmark Results

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