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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 821–830 of 2226 papers

TitleStatusHype
Bayesian Inference on Binary Spiking Networks Leveraging Nanoscale Device Stochasticity—0
Differentially Private Distributed Bayesian Linear Regression with MCMCCode0
Kernel Stein Discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularizationCode0
Adaptive sparseness for correntropy-based robust regression via automatic relevance determination—0
Classified as unknown: A novel Bayesian neural network—0
Optimally-Weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free InferenceCode0
Coin Sampling: Gradient-Based Bayesian Inference without Learning RatesCode0
Projective Integral Updates for High-Dimensional Variational InferenceCode0
Robust Gaussian Process Regression with Huber Likelihood—0
Hierarchical Bayesian inference for community detection and connectivity of functional brain networksCode0
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Benchmark Results

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