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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 576–600 of 2226 papers

TitleStatusHype
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