Nianqiao 'Phyllis' Ju
Nianqiao 'Phyllis' Ju
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data augmentation
SOMA: A Novel Sampler for Bayesian Inference from Privatized Data
Making valid statistical inferences from privatized data is a key challenge in modern analysis. In Bayesian settings, data augmentation MCMC (DAMCMC) methods impute unobserved confidential data given noisy privatized summaries, enabling principled …
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