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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 641–650 of 2226 papers

TitleStatusHype
A Practitioner's Guide to Bayesian Inference in Pharmacometrics using Pumas—0
Leveraging joint sparsity in hierarchical Bayesian learningCode0
Training Language Models with Language Feedback at ScaleCode1
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
A statistical framework for GWAS of high dimensional phenotypes using summary statistics, with application to metabolite GWAS—0
Posterior Estimation Using Deep Learning: A Simulation Study of Compartmental Modeling in Dynamic PET—0
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Benchmark Results

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