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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 471–480 of 2226 papers

TitleStatusHype
ForecastPFN: Synthetically-Trained Zero-Shot ForecastingCode1
Robust and Conjugate Gaussian Process RegressionCode0
Projecting basis functions with tensor networks for Gaussian process regression—0
Inferring to C or not to C: Evolutionary games with Bayesian inferential strategies—0
Function Space Bayesian Pseudocoreset for Bayesian Neural Networks—0
Hierarchical Semi-Implicit Variational Inference with Application to Diffusion Model AccelerationCode0
Bayesian Neural Controlled Differential Equations for Treatment Effect EstimationCode0
Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian InferenceCode0
Bayesian imaging inverse problem with SA-Roundtrip prior via HMC-pCN samplerCode0
Amortised Inference in Neural Networks for Small-Scale Probabilistic Meta-Learning—0
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Benchmark Results

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