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A variational neural Bayes framework for inference on intractable posterior distributions
In this paper, we propose a framework for Bayesian posterior estimation by mapping data to posteriors of parameters using a neural network trained on data simulated from the complex model.
A statistical framework for GWAS of high dimensional phenotypes using summary statistics, with application to metabolite GWAS
The recent explosion of genetic and high dimensional biobank and 'omic' data has provided researchers with the opportunity to investigate the shared genetic origin (pleiotropy) of hundreds to thousands of related phenotypes.
Transfer Learning with Uncertainty Quantification: Random Effect Calibration of Source to Target (RECaST)
Most existing transfer learning approaches are based on fine-tuning pre-trained neural network models, and fail to provide crucial uncertainty quantification.
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