dapper: Data Augmentation for Private Posterior Estimation in R

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書誌詳細
出版年:	arXiv.org (Dec 19, 2024), p. n/a
第一著者:	Eng, Kevin
その他の著者:	Awan, Jordan A, Nianqiao, Phyllis Ju, Rao, Vinayak A, Gong, Ruobin
出版事項:	Cornell University Library, arXiv.org
主題:	Data augmentation Markov chains Parameter estimation Bayesian analysis Statistical analysis Statistical models Statistical inference Monte Carlo simulation Privacy
オンライン･アクセス:	Citation/Abstract Full text outside of ProQuest
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その他の書誌記述
抄録:	This paper serves as a reference and introduction to using the R package dapper. dapper encodes a sampling framework which allows exact Markov chain Monte Carlo simulation of parameters and latent variables in a statistical model given privatized data. The goal of this package is to fill an urgent need by providing applied researchers with a flexible tool to perform valid Bayesian inference on data protected by differential privacy, allowing them to properly account for the noise introduced for privacy protection in their statistical analysis. dapper offers a significant step forward in providing general-purpose statistical inference tools for privatized data.
ISSN:	2331-8422
ソース:	Engineering Database