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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 1426–1450 of 2226 papers

TitleStatusHype
Variational Bayesian Inference for Crowdsourcing Predictions—0
Variational Bayesian Inference For A Scale Mixture Of Normal Distributions Handling Missing Data—0
Variational Bayesian Inference for Audio-Visual Tracking of Multiple Speakers—0
Variational Bayesian inference for CP tensor completion with side information—0
Variational Bayesian Inference for Hidden Markov Models With Multivariate Gaussian Output Distributions—0
Variational Bayesian Inference for Robust Streaming Tensor Factorization and Completion—0
Variational Bayesian Inference for Tensor Robust Principal Component Analysis—0
Variational Bayesian Inference for Time-Varying Massive MIMO Channels: Estimation and Detection—0
Variational Bayesian inference of hidden stochastic processes with unknown parameters—0
Variational Bayesian Inference of Line Spectra—0
Variational Bayesian Inference with Stochastic Search—0
Variational Bayesian surrogate modelling with application to robust design optimisation—0
Variational Density Propagation Continual Learning—0
Variational Domain Adaptation—0
Variational Gaussian Dropout is not Bayesian—0
Variational Hamiltonian Monte Carlo via Score Matching—0
Variational Inference as Iterative Projection in a Bayesian Hilbert Space with Application to Robotic State Estimation—0
Variational Inference for Bayesian Bridge Regression—0
Variational Inference for GARCH-family Models—0
Variational Inference for Gaussian Process Modulated Poisson Processes—0
Variational Inference for Latent Variable Models in High Dimensions—0
Variational Inference for Model-Free and Model-Based Reinforcement Learning—0
Variational Inference for Quantum HyperNetworks—0
Variational Inference for Sparse and Undirected Models—0
Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians—0
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Benchmark Results

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