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 1291–1300 of 2226 papers

TitleStatusHype
Bayesian PseudocoresetsCode0
Online Bayesian Goal Inference for Boundedly Rational Planning Agents—0
Distributed Variational Bayesian Algorithms Over Sensor Networks—0
Free Energy Minimization: A Unified Framework for Modelling, Inference, Learning,and Optimization—0
Variational Refinement for Importance SamplingUsing the Forward Kullback-Leibler Divergence—0
On the Inconsistency of Bayesian Inference for Misspecified Neural Networks—0
Parametric Bootstrap Ensembles as Variational Inference—0
Transforming Worlds: Automated Involutive MCMC for Open-Universe Probabilistic Models—0
Gaussian Density Parametrization Flow: Particle and Stochastic Approaches—0
Understanding Variational Inference in Function-SpaceCode0
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Benchmark Results

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