9 citations · 15 across the 10 of their papers we have counts for
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stat.ME2025
SOMA: A Novel Sampler for Bayesian Inference from Privatized Data
Yifei Xiong, Nianqiao Phyllis Ju
Making valid statistical inferences from privatized data is a key challenge in modern analysis. In Bayesian settings, data augmentation MCMC (DAMCMC) methods impute unobserved conf…
stat.ME2022★ 9 cited
Data Augmentation MCMC for Bayesian Inference from Privatized Data
Nianqiao Ju, Jordan A. Awan, Ruobin Gong +1
Differentially private mechanisms protect privacy by introducing additional randomness into the data. Restricting access to only the privatized data makes it challenging to perform…