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 1761–1770 of 2226 papers

TitleStatusHype
Bayesian filtering unifies adaptive and non-adaptive neural network optimization methodsCode0
Online Label Aggregation: A Variational Bayesian Approach—0
Spatio-Temporal Structured Sparse Regression with Hierarchical Gaussian Process Priors—0
Fast yet Simple Natural-Gradient Descent for Variational Inference in Complex ModelsCode0
A Driver Behavior Modeling Structure Based on Non-parametric Bayesian Stochastic Hybrid Architecture—0
A Stochastic Hybrid Framework for Driver Behavior Modeling Based on Hierarchical Dirichlet Process—0
Accelerated physical emulation of Bayesian inference in spiking neural networks—0
Rating Distributions and Bayesian Inference: Enhancing Cognitive Models of Spatial Language Use—0
Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF)—0
Scalable approximate Bayesian inference for particle tracking dataCode0
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Benchmark Results

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