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 1–10 of 2226 papers

TitleStatusHype
BlackJAX: Composable Bayesian inference in JAXCode5
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a SecondCode5
sbi reloaded: a toolkit for simulation-based inference workflowsCode4
Statistical Machine Learning for Astronomy -- A TextbookCode2
Stable Derivative Free Gaussian Mixture Variational Inference for Bayesian Inverse ProblemsCode2
Empower Structure-Based Molecule Optimization with Gradient Guided Bayesian Flow NetworksCode2
BSD: a Bayesian framework for parametric models of neural spectraCode2
Efficient, Multimodal, and Derivative-Free Bayesian Inference With Fisher-Rao Gradient FlowsCode2
DistPred: A Distribution-Free Probabilistic Inference Method for Regression and ForecastingCode2
The future of cosmological likelihood-based inference: accelerated high-dimensional parameter estimation and model comparisonCode2
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Benchmark Results

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