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Found 4 papers, 2 papers with code
Federated Learning for Non-factorizable Models using Deep Generative Prior Approximations
We demonstrate the SIGMA prior's effectiveness on synthetic data and showcase its utility in a real-world example of FL for spatial data, using a conditional autoregressive prior to model spatial dependence across Australia.
Deep learning and MCMC with aggVAE for shifting administrative boundaries: mapping malaria prevalence in Kenya
Model-based disease mapping remains a fundamental policy-informing tool in the fields of public health and disease surveillance.
PriorCVAE: scalable MCMC parameter inference with Bayesian deep generative modelling
Recent advances have shown that GP priors, or their finite realisations, can be encoded using deep generative models such as variational autoencoders (VAEs).
PriorVAE: Encoding spatial priors with VAEs for small-area estimation
Gaussian processes (GPs), implemented through multivariate Gaussian distributions for a finite collection of data, are the most popular approach in small-area spatial statistical modelling.
