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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 1401–1410 of 2226 papers

TitleStatusHype
Fast Convergence for Langevin with Matrix Manifold Structure—0
Bayesian Inference in High-Dimensional Time-varying Parameter Models using Integrated Rotated Gaussian Approximations—0
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU NetworksCode1
Generalized Bayesian Filtering via Sequential Monte Carlo—0
Safe Imitation Learning via Fast Bayesian Reward Inference from PreferencesCode1
πVAE: a stochastic process prior for Bayesian deep learning with MCMCCode1
Sequential Cooperative Bayesian Inference—0
Fast Convergence for Langevin Diffusion with Manifold Structure—0
Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization—0
Domain Adaptation as a Problem of Inference on Graphical ModelsCode1
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Benchmark Results

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