dapper: Data Augmentation for Private Posterior Estimation in R

محفوظ في:

التفاصيل البيبلوغرافية
الحاوية / القاعدة:	arXiv.org (Dec 19, 2024), p. n/a
المؤلف الرئيسي:	Eng, Kevin
مؤلفون آخرون:	Awan, Jordan A, Nianqiao, Phyllis Ju, Rao, Vinayak A, Gong, Ruobin
منشور في:	Cornell University Library, arXiv.org
الموضوعات:	Data augmentation Markov chains Parameter estimation Bayesian analysis Statistical analysis Statistical models Statistical inference Monte Carlo simulation Privacy
الوصول للمادة أونلاين:	Citation/Abstract Full text outside of ProQuest
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

الوصف
مستخلص:	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.
تدمد:	2331-8422
المصدر:	Engineering Database