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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 2071–2080 of 2226 papers

TitleStatusHype
Dynamics on Lie groups with applications to attitude estimation—0
Efficient acquisition rules for model-based 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
Efficient Bayesian Inference for Nested Simulators—0
Efficient Bayesian inference using physics-informed invertible neural networks for inverse problems—0
Efficient Bayesian species tree inference under the multi-species coalescent—0
Efficient Bayesian synthetic likelihood with whitening transformations—0
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Benchmark Results

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