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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 1051–1075 of 2226 papers

TitleStatusHype
A Bayesian/Information Theoretic Model of Bias Learning—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
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC—0
Diffusion-based supervised learning of generative models for efficient sampling of multimodal distributions—0
Bayesian Inference Accelerator for Spiking Neural Networks—0
Annealing Flow Generative Models Towards Sampling High-Dimensional and Multi-Modal Distributions—0
Differentially private training of neural networks with Langevin dynamics for calibrated predictive uncertainty—0
Bayesian Incremental Inference Update by Re-using Calculations from Belief Space Planning: A New Paradigm—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 Bayesian Inference for Generalized Linear Models—0
Bayesian imaging using Plug & Play priors: when Langevin meets Tweedie—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
DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs—0
Device Detection and Channel Estimation in MTC with Correlated Activity Pattern—0
Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution—0
Development of Bayesian Component Failure Models in E1 HEMP Grid Analysis—0
Bayesian Hypernetworks—0
An Interpretable Neural Network for Parameter Inference—0
Adaptive Synaptic Failure Enables Sampling from Posterior Predictive Distributions in the Brain—0
Developing and Testing a Bayesian Analysis of Fluorescence Lifetime Measurements—0
Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference—0
Deterministic and statistical calibration of constitutive models from full-field data with parametric physics-informed neural networks—0
A New Parameterized Family of Stochastic Particle Flow Filters—0
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Benchmark Results

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