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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 1481–1490 of 2226 papers

TitleStatusHype
Why bigger is not always better: on finite and infinite neural networks—0
Winner-Take-All as Basic Probabilistic Inference Unit of Neuronal Circuits—0
Worst-Case Analysis is Maximum-A-Posteriori Estimation—0
Zero-Truncated Poisson Tensor Factorization for Massive Binary Tensors—0
Subgroup-based Rank-1 Lattice Quasi-Monte Carlo—0
Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting—0
Sparse Bayesian Learning Approach for Discrete Signal Reconstruction—0
Uncertainty Quantification with Statistical Guarantees in End-to-End Autonomous Driving Control—0
A Hamilton-Jacobi Reachability-Based Framework for Predicting and Analyzing Human Motion for Safe Planning—0
Meta-Posterior Consistency for the Bayesian Inference of Metastable System—0
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Benchmark Results

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