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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 1551–1575 of 2226 papers

TitleStatusHype
Adaptive Gaussian Copula ABC—0
Adaptive Gaussian process surrogates for Bayesian inference—0
Adaptive matching pursuit for sparse signal recovery—0
Adaptive mitigation of time-varying quantum noise—0
Adaptive modelling of anti-tau treatments for neurodegenerative disorders based on the Bayesian approach with physics-informed neural networks—0
Adaptive particle-based approximations of the Gibbs posterior for inverse problems—0
Adaptive posterior distributions for uncertainty analysis of covariance matrices in Bayesian inversion problems for multioutput signals—0
Adaptive quadrature schemes for Bayesian inference via active learning—0
Adaptive sparseness for correntropy-based robust regression via automatic relevance determination—0
Adaptive Synaptic Failure Enables Sampling from Posterior Predictive Distributions in the Brain—0
Addressing Census data problems in race imputation via fully Bayesian Improved Surname Geocoding and name supplements—0
A Deeper Look at the Unsupervised Learning of Disentangled Representations in β-VAE from the Perspective of Core Object Recognition—0
A Deep Learning Approach to Dst Index Prediction—0
A deep learning framework for geodesics under spherical Wasserstein-Fisher-Rao metric and its application for weighted sample generation—0
A deep surrogate approach to efficient Bayesian inversion in PDE and integral equation models—0
A Distributed Framework for the Construction of Transport Maps—0
Adjoint-aided inference of Gaussian process driven differential equations—0
A Driver Behavior Modeling Structure Based on Non-parametric Bayesian Stochastic Hybrid Architecture—0
Advancing Audio Emotion and Intent Recognition with Large Pre-Trained Models and Bayesian Inference—0
Advancing Autonomous Vehicle Safety: A Combined Fault Tree Analysis and Bayesian Network Approach—0
Adversarial Message Passing For Graphical Models—0
A Factor Graph Model of Trust for a Collaborative Multi-Agent System—0
A theory of representation learning gives a deep generalisation of kernel methods—0
Affine Invariant Ensemble Transform Methods to Improve Predictive Uncertainty in Neural Networks—0
A Formal Calculus for International Relations Computation and Evaluation—0
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Benchmark Results

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