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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 741–750 of 2226 papers

TitleStatusHype
Bottom-up data integration in polymer models of chromatin organisation—0
Autoencoded sparse Bayesian in-IRT factorization, calibration, and amortized inference for the Work Disability Functional Assessment Battery—0
On Representations of Mean-Field Variational Inference—0
Variational Model Perturbation for Source-Free Domain AdaptationCode0
Data Subsampling for Bayesian Neural NetworksCode0
Deep Learning Aided Laplace Based Bayesian Inference for Epidemiological Systems—0
Marginalized particle Gibbs for multiple state-space models coupled through shared parameters—0
On Divergence Measures for Bayesian PseudocoresetsCode0
Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion ModelsCode0
Sampling-based inference for large linear models, with application to linearised LaplaceCode0
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Benchmark Results

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