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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 501–525 of 2226 papers

TitleStatusHype
STAMP: Differentiable Task and Motion Planning via Stein Variational Gradient Descent—0
Statistical physics, Bayesian inference and neural information processing—0
A Mean Field Approach to Empirical Bayes Estimation in High-dimensional Linear Regression—0
A Primer on Bayesian Neural Networks: Review and DebatesCode1
A Metaheuristic for Amortized Search in High-Dimensional Parameter SpacesCode0
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
BayesDLL: Bayesian Deep Learning LibraryCode1
Bounded rationality in structured density estimation—0
Bounded rationality in structured density estimation—0
MFRL-BI: Design of a Model-free Reinforcement Learning Process Control Scheme by Using Bayesian Inference—0
Data-driven Modeling and Inference for Bayesian Gaussian Process ODEs via Double Normalizing FlowsCode0
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
Parameterizing pressure-temperature profiles of exoplanet atmospheres with neural networksCode0
Learning Active Subspaces for Effective and Scalable Uncertainty Quantification in Deep Neural Networks—0
Bayesian inference of composition-dependent phase diagrams—0
PAVI: Plate-Amortized Variational Inference—0
Heterogeneous Multi-Task Gaussian Cox ProcessesCode0
Deep Learning and Bayesian inference for Inverse Problems—0
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Benchmark Results

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