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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 851–860 of 2226 papers

TitleStatusHype
Episodic memory for continual model learning—0
Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning—0
Efficient acquisition rules for model-based approximate Bayesian computation—0
Bayesian inference for spatio-temporal spike-and-slab priors—0
A Parzen-based distance between probability measures as an alternative of summary statistics in Approximate Bayesian Computation—0
Efficient Approximate Inference with Walsh-Hadamard Variational Inference—0
Efficient Attack Graph Analysis through Approximate Inference—0
Efficient Bayesian Computation Using Plug-and-Play Priors for Poisson Inverse Problems—0
Efficient Bayesian Inference for a Gaussian Process Density Model—0
Excess risk analysis for epistemic uncertainty with application to variational inference—0
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Benchmark Results

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