SOTAVerified

Bayesian Inference

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

Papers

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Showing 1461–1470 of 2226 papers

TitleStatusHype
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
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Benchmark Results

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