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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 776–800 of 2226 papers

TitleStatusHype
De-randomizing MCMC dynamics with the diffusion Stein operator—0
Attention-Aware Answers of the Crowd—0
Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks: An Extended Investigation—0
A New Parameterized Family of Stochastic Particle Flow Filters—0
Deterministic and statistical calibration of constitutive models from full-field data with parametric physics-informed neural networks—0
Beyond Prior Limits: Addressing Distribution Misalignment in Particle Filtering—0
Developing and Testing a Bayesian Analysis of Fluorescence Lifetime Measurements—0
Development of Bayesian Component Failure Models in E1 HEMP Grid Analysis—0
Bayesian Hypernetworks—0
Device Detection and Channel Estimation in MTC with Correlated Activity Pattern—0
DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs—0
Diagnosing model misspecification and performing generalized Bayes' updates via probabilistic classifiers—0
An Introduction to Animal Movement Modeling with Hidden Markov Models using Stan for Bayesian Inference—0
Differentially Private Bayesian Inference for Generalized Linear Models—0
Bayesian imaging using Plug & Play priors: when Langevin meets Tweedie—0
Bayesian Imaging With Data-Driven Priors Encoded by Neural Networks: Theory, Methods, and Algorithms—0
Addressing Census data problems in race imputation via fully Bayesian Improved Surname Geocoding and name supplements—0
Differentially private training of neural networks with Langevin dynamics for calibrated predictive uncertainty—0
A Bayesian take on option pricing with Gaussian processes—0
Diffusion-based supervised learning of generative models for efficient sampling of multimodal distributions—0
A comparison of Bayesian sampling algorithms for high-dimensional particle physics and cosmology applications—0
A Trust-Region Method for Graphical Stein Variational Inference—0
Digital Twin Framework for Optimal and Autonomous Decision-Making in Cyber-Physical Systems: Enhancing Reliability and Adaptability in the Oil and Gas Industry—0
Sparse Bayesian Learning Approach for Discrete Signal Reconstruction—0
Beta Residuals: Improving Fault-Tolerant Control for Sensory Faults via Bayesian Inference and Precision Learning—0
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Benchmark Results

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