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 791–800 of 2226 papers

TitleStatusHype
Towards Reliable Uncertainty Quantification via Deep Ensembles in Multi-output Regression Task—0
Particle Mean Field Variational BayesCode0
Random-effects substitution models for phylogenetics via scalable gradient approximationsCode0
Dynamical Hyperspectral Unmixing with Variational Recurrent Neural NetworksCode0
Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference—0
Robust probabilistic inference via a constrained transport metricCode0
Posterior Estimation Using Deep Learning: A Simulation Study of Compartmental Modeling in Dynamic PET—0
A statistical framework for GWAS of high dimensional phenotypes using summary statistics, with application to metabolite GWAS—0
Upper Bound of Real Log Canonical Threshold of Tensor Decomposition and its Application to Bayesian Inference—0
Simulation-based Bayesian inference for robotic grasping—0
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Benchmark Results

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