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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 1311–1320 of 2226 papers

TitleStatusHype
Modeling Stochastic Microscopic Traffic Behaviors: a Physics Regularized Gaussian Process Approach—0
SBI -- A toolkit for simulation-based inference—0
Faster Uncertainty Quantification for Inverse Problems with Conditional Normalizing Flows—0
A Class of Conjugate Priors for Multinomial Probit Models which Includes the Multivariate Normal One—0
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian ProcessesCode1
BaCOUn: Bayesian Classifers with Out-of-Distribution Uncertainty—0
Characteristics of Monte Carlo Dropout in Wide Neural Networks—0
Fast and Accurate Forecasting of COVID-19 Deaths Using the SIkJα ModelCode1
Variational Inference with Continuously-Indexed Normalizing FlowsCode0
URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural NetworksCode1
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Benchmark Results

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