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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 901–910 of 2226 papers

TitleStatusHype
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification—0
Uncovering Regions of Maximum Dissimilarity on Random Process Data—0
BayesLDM: A Domain-Specific Language for Probabilistic Modeling of Longitudinal Data—0
Batch Bayesian Optimization via Particle Gradient FlowsCode0
Implicit Full Waveform Inversion with Deep Neural Representation—0
Non-Gaussian Process Regression—0
Bayesian Neural Network Inference via Implicit Models and the Posterior Predictive Distribution—0
Topology Change Aware Data-Driven Probabilistic Distribution State Estimation Based on Gaussian Process—0
Deep importance sampling using tensor trains with application to a priori and a posteriori rare event estimation—0
Investigating the Impact of Model Misspecification in Neural Simulation-based Inference—0
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Benchmark Results

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