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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 326–350 of 2226 papers

TitleStatusHype
Near-Optimal Approximations for Bayesian Inference in Function SpaceCode0
Near-Field Motion Parameter Estimation: A Variational Bayesian Approach—0
Generalization Certificates for Adversarially Robust Bayesian Linear Regression—0
Confidence Estimation via Sequential Likelihood Mixing—0
Evidence of Replica Symmetry Breaking under the Nishimori conditions in epidemic inference on graphs—0
Bayesian Physics Informed Neural Networks for Linear Inverse problems—0
In-Context Parametric Inference: Point or Distribution Estimators?—0
Bayesian inference from time series of allele frequency data using exact simulation techniques—0
Epidemic-guided deep learning for spatiotemporal forecasting of Tuberculosis outbreakCode0
Multifidelity Simulation-based Inference for Computationally Expensive Simulators—0
False Discovery Rate Control via Frequentist-assisted Horseshoe—0
Does Unsupervised Domain Adaptation Improve the Robustness of Amortized Bayesian Inference? A Systematic Evaluation—0
Generalised Bayesian distance-based phylogenetics for the genomics eraCode0
Distribution learning via neural differential equations: minimal energy regularization and approximation theory—0
Posterior SBC: Simulation-Based Calibration Checking Conditional on DataCode0
Constrained belief updates explain geometric structures in transformer representations—0
Distribution Transformers: Fast Approximate Bayesian Inference With On-The-Fly Prior Adaptation—0
Optimal Subspace Inference for the Laplace Approximation of Bayesian Neural NetworksCode0
Eliciting Language Model Behaviors with Investigator Agents—0
Leveraging Joint Predictive Embedding and Bayesian Inference in Graph Self Supervised LearningCode0
Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems—0
Biogeochemistry-Informed Neural Network (BINN) for Improving Accuracy of Model Prediction and Scientific Understanding of Soil Organic Carbon—0
VKFPos: A Learning-Based Monocular Positioning with Variational Bayesian Extended Kalman Filter IntegrationCode0
BARNN: A Bayesian Autoregressive and Recurrent Neural Network—0
Beyond Prior Limits: Addressing Distribution Misalignment in Particle Filtering—0
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Benchmark Results

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