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 1801–1810 of 2226 papers

TitleStatusHype
Bayesian Neural Controlled Differential Equations for Treatment Effect EstimationCode0
Uncertainty Prediction Neural Network (UpNet): Embedding Artificial Neural Network in Bayesian Inversion Framework to Quantify the Uncertainty of Remote Sensing RetrievalCode0
Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with β-DivergencesCode0
DPER: Dynamic Programming for Exist-Random Stochastic SATCode0
DP-Fast MH: Private, Fast, and Accurate Metropolis-Hastings for Large-Scale Bayesian InferenceCode0
Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture ModelsCode0
DPO: Dynamic-Programming Optimization on Hybrid ConstraintsCode0
The Polynomial Stein Discrepancy for Assessing Moment ConvergenceCode0
DropConnect Is Effective in Modeling Uncertainty of Bayesian Deep NetworksCode0
Simulation-based Bayesian Inference from Privacy Protected DataCode0
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Benchmark Results

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