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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 676–700 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
Affine Invariant Ensemble Transform Methods to Improve Predictive Uncertainty in Neural Networks—0
Reducing the False Positive Rate Using Bayesian Inference in Autonomous Driving Perception—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
Parameterizing pressure-temperature profiles of exoplanet atmospheres with neural networksCode0
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
A transport approach to sequential simulation-based inference—0
Auto-weighted Bayesian Physics-Informed Neural Networks and robust estimations for multitask inverse problems in pore-scale imaging of dissolution—0
Variational Density Propagation Continual Learning—0
Linking fast and slow: the case for generative models—0
On Exact Bayesian Credible Sets for Classification and Pattern Recognition—0
On Estimating the Gradient of the Expected Information Gain in Bayesian Experimental DesignCode0
Semi-Implicit Variational Inference via Score MatchingCode0
Accelerated Bayesian imaging by relaxed proximal-point Langevin samplingCode0
Bayesian polynomial neural networks and polynomial neural ordinary differential equations—0
Adaptive mitigation of time-varying quantum noise—0
Natural Evolution Strategies as a Black Box Estimator for Stochastic Variational Inference—0
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Benchmark Results

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