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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 1276–1300 of 2226 papers

TitleStatusHype
Deep learning-based prediction of kinetic parameters from myocardial perfusion MRI—0
Deep Learning Surrogates for Real-Time Gas Emission Inversion—0
Deep Maxout Network Gaussian Process—0
Deep Network Regularization via Bayesian Inference of Synaptic Connectivity—0
Deep Neural Networks as Point Estimates for Deep Gaussian Processes—0
Deep reinforcement learning driven inspection and maintenance planning under incomplete information and constraints—0
DeepRV: pre-trained spatial priors for accelerated disease mapping—0
Deep Stable neural networks: large-width asymptotics and convergence rates—0
Demystifying excessively volatile human learning: A Bayesian persistent prior and a neural approximation—0
Density Estimation via Bayesian Inference Engines—0
Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation—0
De-randomizing MCMC dynamics with the diffusion Stein operator—0
Designing Perceptual Puzzles by Differentiating Probabilistic Programs—0
Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks: An Extended Investigation—0
Deterministic and statistical calibration of constitutive models from full-field data with parametric physics-informed neural networks—0
Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference—0
Developing and Testing a Bayesian Analysis of Fluorescence Lifetime Measurements—0
Development of Bayesian Component Failure Models in E1 HEMP Grid Analysis—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
Differentially Private Bayesian Inference for Generalized Linear Models—0
Differentially private training of neural networks with Langevin dynamics for calibrated predictive uncertainty—0
Diffusion-based supervised learning of generative models for efficient sampling of multimodal distributions—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
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Benchmark Results

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