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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 1201–1250 of 2226 papers

TitleStatusHype
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
Variational Inference MPC for Bayesian Model-based Reinforcement Learning—0
Variational Inference Using Material Point Method—0
Variational Inference via Smoothed Particle Hydrodynamics—0
Variational Multi-Task Learning—0
Variational Neuron Shifting for Few-Shot Image Classification Across Domains—0
Variational Nonlinear Kalman Filtering with Unknown Process Noise Covariance—0
Variational Prediction—0
Variational Predictive Information Bottleneck—0
Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence—0
Variational Refinement for Importance SamplingUsing the Forward Kullback-Leibler Divergence—0
Variational State-Space Models for Localisation and Dense 3D Mapping in 6 DoF—0
Variational Tracking and Prediction with Generative Disentangled State-Space Models—0
Variation Bayesian Interference for Multiple Extended Targets or Unresolved Group Targets Tracking—0
Varying-coefficient models with isotropic Gaussian process priors—0
Vector autoregression models with skewness and heavy tails—0
Vectorial Dimension Reduction for Tensors Based on Bayesian Inference—0
Vehicle Lane Change Prediction based on Knowledge Graph Embeddings and Bayesian Inference—0
Verbalized Probabilistic Graphical Modeling with Large Language Models—0
W2WNet: a two-module probabilistic Convolutional Neural Network with embedded data cleansing functionality—0
Walsh-Hadamard Variational Inference for Bayesian Deep Learning—0
Wasserstein Measure Coresets—0
Wasserstein-Splitting Gaussian Process Regression for Heterogeneous Online Bayesian Inference—0
Wasserstein Variational Inference—0
WattScale: A Data-driven Approach for Energy Efficiency Analytics of Buildings at Scale—0
Weakly-supervised Localization of Manipulated Image Regions Using Multi-resolution Learned Features—0
Webservices for Bayesian Learning—0
Weighted Community Detection and Data Clustering Using Message Passing—0
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Benchmark Results

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