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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 801–810 of 2226 papers

TitleStatusHype
Upper Bound of Real Log Canonical Threshold of Tensor Decomposition and its Application to Bayesian Inference—0
Fast post-process Bayesian inference with Variational Sparse Bayesian QuadratureCode0
Scalable Stochastic Gradient Riemannian Langevin Dynamics in Non-Diagonal MetricsCode0
Bayesian at heart: Towards autonomic outflow estimation via generative state-space modelling of heart rate dynamicsCode0
Federated Learning via Variational Bayesian Inference: Personalization, Sparsity and Clustering—0
A normative theory of social conflict—0
Bayesian inference with finitely wide neural networks—0
Bayesian Posterior Perturbation Analysis with Integral Probability Metrics—0
Joint Task and Data Oriented Semantic Communications: A Deep Separate Source-channel Coding Scheme—0
An overview of differentiable particle filters for data-adaptive sequential Bayesian inference—0
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Benchmark Results

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