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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 1401–1450 of 2226 papers

TitleStatusHype
Uncovering Regions of Maximum Dissimilarity on Random Process Data—0
Underdetermined DOA Estimation of Off-Grid Sources Based on the Generalized Double Pareto Prior—0
Understanding and mitigating difficulties in posterior predictive evaluation—0
Understanding Approximation for Bayesian Inference in Neural Networks—0
Understanding Task Representations in Neural Networks via Bayesian Ablation—0
Unified field theoretical approach to deep and recurrent neuronal networks—0
Unified Probabilistic Neural Architecture and Weight Ensembling Improves Model Robustness—0
Unifying Bayesian Inference and Vector Space Models for Improved Decipherment—0
Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift—0
Unrolled denoising networks provably learn optimal Bayesian inference—0
Unsupervised Spectral Learning of WCFG as Low-rank Matrix Completion—0
Unsupervised structured semantic inference for spoken dialog reservation tasks—0
Upper Bound of Real Log Canonical Threshold of Tensor Decomposition and its Application to Bayesian Inference—0
Using Rejuvenation to Improve Particle Filtering for Bayesian Word Segmentation—0
V2X System Architecture Utilizing Hybrid Gaussian Process-based Model Structures—0
Value of Information Analysis via Active Learning and Knowledge Sharing in Error-Controlled Adaptive Kriging—0
Value of Information and Reward Specification in Active Inference and POMDPs—0
VarFA: A Variational Factor Analysis Framework For Efficient Bayesian Learning Analytics—0
Variance Reduction and Quasi-Newton for Particle-Based Variational Inference—0
Variational approximations using Fisher divergence—0
Variational Bayes: A report on approaches and applications—0
Variational Bayes Decomposition for Inverse Estimation with Superimposed Multispectral Intensity—0
Variational Bayes Deep Operator Network: A data-driven Bayesian solver for parametric differential equations—0
Variational Bayes for Merging Noisy Databases—0
Variational Bayesian Inference Clustering Based Joint User Activity and Data Detection for Grant-Free Random Access in mMTC—0
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