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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 1376–1400 of 2226 papers

TitleStatusHype
A Practical Introduction to Bayesian Estimation of Causal Effects: Parametric and Nonparametric ApproachesCode1
Bayesian differential programming for robust systems identification under uncertaintyCode1
Bayesian Consensus: Consensus Estimates from Miscalibrated Instruments under Heteroscedastic Noise—0
Scaling Bayesian inference of mixed multinomial logit models to very large datasets—0
Model Uncertainty Quantification for Reliable Deep Vision Structural Health Monitoring—0
Active recursive Bayesian inference using Rényi information measures—0
B-SCST: Bayesian Self-Critical Sequence Training for Image Captioning—0
The equivalence between Stein variational gradient descent and black-box variational inference—0
Regression Approach for Modeling COVID-19 Spread and its Impact On Stock Market—0
Inferring COVID-19 spreading rates and potential change points for case number forecastsCode1
Supervised Learning with Quantum MeasurementsCode1
Bayesian ODE Solvers: The Maximum A Posteriori Estimate—0
GAN-based Priors for Quantifying UncertaintyCode1
Too many cooks: Bayesian inference for coordinating multi-agent collaborationCode1
A Bayesian brain model of adaptive behavior: An application to the Wisconsin Card Sorting Task—0
Semi-Modular Inference: enhanced learning in multi-modular models by tempering the influence of componentsCode0
BayesFlow: Learning complex stochastic models with invertible neural networksCode1
An Empirical Evaluation on Robustness and Uncertainty of Regularization Methods—0
The Variational InfoMax Learning Objective—0
Deep Active Inference for Autonomous Robot Navigation—0
A Bayesian algorithm for retrosynthesisCode1
Flexible Bayesian Nonlinear Model ConfigurationCode1
MPC-guided Imitation Learning of Neural Network Policies for the Artificial PancreasCode0
PlaNet of the Bayesians: Reconsidering and Improving Deep Planning Network by Incorporating Bayesian Inference—0
MC^2RAM: Markov Chain Monte Carlo Sampling in SRAM for Fast Bayesian Inference—0
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Benchmark Results

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