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 1661–1670 of 2226 papers

TitleStatusHype
Manifold Optimization Assisted Gaussian Variational Approximation—0
Marginal and Joint Cross-Entropies & Predictives for Online Bayesian Inference, Active Learning, and Active Sampling—0
Marginalized particle Gibbs for multiple state-space models coupled through shared parameters—0
Marginalizing Corrupted Features—0
Marginal likelihood and model selection for Gaussian latent tree and forest models—0
Marginal sequential Monte Carlo for doubly intractable models—0
Markov Chain Monte Carlo and Variational Inference: Bridging the Gap—0
Markov Regime-Switching Intelligent Driver Model for Interpretable Car-Following Behavior—0
Martingale Posterior Neural Processes—0
Massively Parallel Reweighted Wake-Sleep—0
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Benchmark Results

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