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 1421–1430 of 2226 papers

TitleStatusHype
Few-Shot Bayesian Imitation Learning with Logical Program Policies—0
Few-shot Non-line-of-sight Imaging with Signal-surface Collaborative Regularization—0
Finite-Dimensional BFRY Priors and Variational Bayesian Inference for Power Law Models—0
Finite Horizon Throughput Maximization and Sensing Optimization in Wireless Powered Devices over Fading Channels—0
Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection—0
Firefly Monte Carlo: Exact MCMC with Subsets of Data—0
Fishnets: Information-Optimal, Scalable Aggregation for Sets and Graphs—0
Flexible and Hierarchical Prior for Bayesian Nonnegative Matrix Factorization—0
Flexible Bayesian Inference on Partially Observed Epidemics—0
Flexible Mixture Modeling on Constrained Spaces—0
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Benchmark Results

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