SOTAVerified

Bayesian Inference

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

Papers

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Showing 1491–1500 of 2226 papers

TitleStatusHype
Not All Claims are Created Equal: Choosing the Right Statistical Approach to Assess HypothesesCode0
Machine Learning using the Variational Predictive Information Bottleneck with a Validation Set—0
Variational Bayesian inference of hidden stochastic processes with unknown parameters—0
Phase transitions and optimal algorithms for semi-supervised classifications on graphs: from belief propagation to graph convolution network—0
Continual Multi-task Gaussian ProcessesCode0
Parameter elimination in particle Gibbs samplingCode0
Bayesian causal inference via probabilistic program synthesis—0
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
Sampling of Bayesian posteriors with a non-Gaussian probabilistic learning on manifolds from a small dataset—0
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Benchmark Results

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