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 926–950 of 2226 papers

TitleStatusHype
Evidence of Replica Symmetry Breaking under the Nishimori conditions in epidemic inference on graphs—0
FedLog: Personalized Federated Classification with Less Communication and More Flexibility—0
Few-Shot Bayesian Imitation Learning with Logical Program Policies—0
Exact Bayesian inference for off-line change-point detection in tree-structured graphical models—0
Finite Horizon Throughput Maximization and Sensing Optimization in Wireless Powered Devices over Fading Channels—0
Excess risk analysis for epistemic uncertainty with application to variational inference—0
Exchangeable Variational Autoencoders with Applications to Genomic Data—0
Exoplanet Characterization using Conditional Invertible Neural Networks—0
Bayesian Inference of Transition Matrices from Incomplete Graph Data with a Topological Prior—0
Expectation Propagation performs a smoothed gradient descent—0
Experimentally detecting a quantum change point via Bayesian inference—0
Explainable Lane Change Prediction for Near-Crash Scenarios Using Knowledge Graph Embeddings and Retrieval Augmented Generation—0
Exploiting Dynamic Sparsity for Near-Field Spatial Non-Stationary XL-MIMO Channel Tracking—0
Free energy and inference in living systems—0
Exploration in Interactive Personalized Music Recommendation: A Reinforcement Learning Approach—0
Exploring Action-Centric Representations Through the Lens of Rate-Distortion Theory—0
Bayesian inference on random simple graphs with power law degree distributions—0
A Primer on Variational Inference for Physics-Informed Deep Generative Modelling—0
A theory of representation learning gives a deep generalisation of kernel methods—0
Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference—0
Belief functions induced by random fuzzy sets: A general framework for representing uncertain and fuzzy evidence—0
Extending the statistical software package Engine for Likelihood-Free Inference—0
Extension of compressive sampling to binary vector recovery for model-based defect imaging—0
Factorized Asymptotic Bayesian Inference for Factorial Hidden Markov Models—0
A Tractable Fully Bayesian Method for the Stochastic Block Model—0
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Benchmark Results

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