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dapper: Data Augmentation for Private Posterior Estimation in R

Kaydedildi:
Yayımlandı:arXiv.org (Dec 19, 2024), p. n/a
Yazar: Eng, Kevin
Diğer Yazarlar: Awan, Jordan A, Nianqiao, Phyllis Ju, Rao, Vinayak A, Gong, Ruobin
Baskı/Yayın Bilgisi:
Cornell University Library, arXiv.org
Konular:
Data augmentation
Markov chains
Parameter estimation
Bayesian analysis
Statistical analysis
Statistical models
Statistical inference
Monte Carlo simulation
Privacy
Online Erişim:Citation/Abstract
Full text outside of ProQuest
Etiketler: Etiketle
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Özet: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
Kaynak:Engineering Database