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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 1331–1340 of 2226 papers

TitleStatusHype
The efficacy and generalizability of conditional GANs for posterior inference in physics-based inverse problems—0
The equivalence between Stein variational gradient descent and black-box variational inference—0
The FMRIB Variational Bayesian Inference Tutorial II: Stochastic Variational Bayes—0
Fundamental limits and algorithms for sparse linear regression with sublinear sparsity—0
The high-conductance state enables neural sampling in networks of LIF neurons—0
The Inverse of Exact Renormalization Group Flows as Statistical Inference—0
The Kikuchi Hierarchy and Tensor PCA—0
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks—0
The linear conditional expectation in Hilbert space—0
Theoretical and Computational Guarantees of Mean Field Variational Inference for Community Detection—0
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Benchmark Results

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