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 831–840 of 2226 papers

TitleStatusHype
EinSteinVI: General and Integrated Stein Variational Inference—0
Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with -Divergences—0
Embarrassingly parallel MCMC using deep invertible transformations—0
Bayesian Inference for Left-Truncated Log-Logistic Distributions for Time-to-event Data Analysis—0
Enhanced gradient-based MCMC in discrete spaces—0
DPGIIL: Dirichlet Process-Deep Generative Model-Integrated Incremental Learning for Clustering in Transmissibility-based Online Structural Anomaly Detection—0
Bayesian inference for low rank spatiotemporal neural receptive fields—0
Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data—0
Bayesian Inference for Multidimensional Welfare Comparisons—0
ESS-ReduNet: Enhancing Subspace Separability of ReduNet via Dynamic Expansion with Bayesian Inference—0
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Benchmark Results

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