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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 1471–1480 of 2226 papers

TitleStatusHype
Parameter elimination in particle Gibbs samplingCode0
A Hamilton-Jacobi Reachability-Based Framework for Predicting and Analyzing Human Motion for Safe Planning—0
Scalable Inference for Nonparametric Hawkes Process Using Pólya-Gamma Augmentation—0
Stein Variational Gradient Descent With Matrix-Valued KernelsCode0
Sampling of Bayesian posteriors with a non-Gaussian probabilistic learning on manifolds from a small dataset—0
Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning—0
Fast and Flexible Bayesian Inference in Time-varying Parameter Regression Models—0
We Know Where We Don't Know: 3D Bayesian CNNs for Credible Geometric UncertaintyCode0
Variational Predictive Information Bottleneck—0
Safe-Bayesian Generalized Linear Regression—0
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Benchmark Results

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