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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 701–710 of 2226 papers

TitleStatusHype
Gradient-Based Markov Chain Monte Carlo for MIMO Detection—0
A Review of Change of Variable Formulas for Generative Modeling—0
Learning from Topology: Cosmological Parameter Estimation from the Large-scale Structure—0
Pruning a neural network using Bayesian inference—0
The Bayesian Context Trees State Space Model for time series modelling and forecasting—0
A digital twin framework for civil engineering structuresCode0
Simulation-based Inference for High-dimensional Data using Surjective Sequential Neural Likelihood EstimationCode0
Moreau-Yoshida Variational Transport: A General Framework For Solving Regularized Distributional Optimization ProblemsCode0
A theory of data variability in Neural Network Bayesian inference—0
Information-theoretic Analysis of Test Data Sensitivity in Uncertainty—0
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Benchmark Results

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