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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 551–600 of 2226 papers

TitleStatusHype
Scalable Data Assimilation with Message PassingCode0
Neural Methods for Amortized Inference—0
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
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
Interactive Learning of Physical Object Properties Through Robot Manipulation and Database of Object MeasurementsCode0
Efficient Sound Field Reconstruction with Conditional Invertible Neural Networks—0
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 the Sim-to-Real Gap with Bayesian Inference—0
Bridging Privacy and Robustness for Trustworthy Machine Learning—0
Predictive, scalable and interpretable knowledge tracing on structured domainsCode0
Clustered Mallows Model—0
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
Statistical Mechanics of Dynamical System Identification—0
Joint Parameter and Parameterization Inference with Uncertainty Quantification through Differentiable Programming—0
Quasi-Bayesian Estimation and Inference with Control Functions—0
Stochastic Approximation with Biased MCMC for Expectation MaximizationCode0
Sequential transport maps using SoS density estimation and α-divergencesCode0
Prediction of the SYM-H Index Using a Bayesian Deep Learning Method with Uncertainty Quantification—0
Towards a Digital Twin Framework in Additive Manufacturing: Machine Learning and Bayesian Optimization for Time Series Process Optimization—0
DART: Depth-Enhanced Accurate and Real-Time Background Matting—0
Accelerating Convergence of Stein Variational Gradient Descent via Deep UnfoldingCode0
Regularization by denoising: Bayesian model and Langevin-within-split Gibbs sampling—0
Human Goal Recognition as Bayesian Inference: Investigating the Impact of Actions, Timing, and Goal Solvability—0
Semi-parametric financial risk forecasting incorporating multiple realized measures—0
Improvement and generalization of ABCD method with Bayesian inference—0
Bayesian Deep Learning Via Expectation Maximization and Turbo Deep Approximate Message Passing—0
A Factor Graph Model of Trust for a Collaborative Multi-Agent System—0
SMC Is All You Need: Parallel Strong Scaling—0
Recent methods from statistical inference and machine learning to improve integrative modeling of macromolecular assemblies—0
Recovering Mental Representations from Large Language Models with Markov Chain Monte Carlo—0
Dynamical System Identification, Model Selection and Model Uncertainty Quantification by Bayesian Inference—0
Incoherent Probability Judgments in Large Language Models—0
Enhancing Gaussian Process Surrogates for Optimization and Posterior Approximation via Random Exploration—0
Distributed Markov Chain Monte Carlo Sampling based on the Alternating Direction Method of MultipliersCode0
Bayesian Inference Accelerator for Spiking Neural Networks—0
A hybrid tau-leap for simulating chemical kinetics with applications to parameter estimation—0
Quantifying cell cycle regulation by tissue crowdingCode0
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Benchmark Results

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