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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 2211–2220 of 2226 papers

TitleStatusHype
Amortized Bayesian Inference of GISAXS Data with Normalizing FlowsCode0
Improving Neural Additive Models with Bayesian PrinciplesCode0
Laplace approximation for logistic Gaussian process density estimation and regressionCode0
Laplace Matching for fast Approximate Inference in Latent Gaussian ModelsCode0
Bayesian posterior approximation with stochastic ensemblesCode0
Signatures of Bayesian inference emerge from energy efficient synapsesCode0
Large-Scale Stochastic Sampling from the Probability SimplexCode0
Adversarial robustness of amortized Bayesian inferenceCode0
Learning Asymmetric and Local Features in Multi-Dimensional Data through Wavelets with Recursive PartitioningCode0
Latent Optimal Paths by Gumbel Propagation for Variational Bayesian Dynamic ProgrammingCode0
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Benchmark Results

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