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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 551–575 of 2226 papers

TitleStatusHype
Scalable Data Assimilation with Message PassingCode0
BIRD: A Trustworthy Bayesian Inference Framework for Large Language Models—0
Underdetermined DOA Estimation of Off-Grid Sources Based on the Generalized Double Pareto Prior—0
Neural Methods for Amortized Inference—0
Partial Identification of Heteroskedastic Structural VARs: Theory and Bayesian Inference—0
Analytical Approximation of the ELBO Gradient in the Context of the Clutter ProblemCode0
Leveraging viscous Hamilton-Jacobi PDEs for uncertainty quantification in scientific machine learningCode0
Bayesian Federated Model Compression for Communication and Computation Efficiency—0
Efficient Sound Field Reconstruction with Conditional Invertible Neural Networks—0
Interactive Learning of Physical Object Properties Through Robot Manipulation and Database of Object MeasurementsCode0
Efficient Training of Probabilistic Neural Networks for Survival AnalysisCode0
Bayesian Inference for Consistent Predictions in Overparameterized Nonlinear RegressionCode0
Active Exploration in Bayesian Model-based Reinforcement Learning for Robot Manipulation—0
Accounting for contact network uncertainty in epidemic inferences—0
Divide, Conquer, Combine Bayesian Decision Tree Sampling—0
A Unified Kernel for Neural Network Learning—0
Bridging Privacy and Robustness for Trustworthy Machine Learning—0
Bridging the Sim-to-Real Gap with Bayesian Inference—0
Clustered Mallows Model—0
Predictive, scalable and interpretable knowledge tracing on structured domainsCode0
Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware PriorsCode0
Fast, accurate and lightweight sequential simulation-based inference using Gaussian locally linear mappingsCode0
In-context Exploration-Exploitation for Reinforcement Learning—0
Scalable Bayesian inference for the generalized linear mixed model—0
A prediction rigidity formalism for low-cost uncertainties in trained neural networks—0
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Benchmark Results

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