dapper: Data Augmentation for Private Posterior Estimation in R

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Podrobná bibliografie
Vydáno v:	arXiv.org (Dec 19, 2024), p. n/a
Hlavní autor:	Eng, Kevin
Další autoři:	Awan, Jordan A, Nianqiao, Phyllis Ju, Rao, Vinayak A, Gong, Ruobin
Vydáno:	Cornell University Library, arXiv.org
Témata:	Data augmentation Markov chains Parameter estimation Bayesian analysis Statistical analysis Statistical models Statistical inference Monte Carlo simulation Privacy
On-line přístup:	Citation/Abstract Full text outside of ProQuest
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Popis
Abstrakt:	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
Zdroj:	Engineering Database