MCMC for Bayesian nonparametric mixture modeling under differential privacy

IF 1.4 2区数学 Q2 STATISTICS & PROBABILITY

Journal of Computational and Graphical Statistics Pub Date : 2024-10-07 DOI:10.1080/10618600.2024.2410911

Mario Beraha, Stefano Favaro, Vinayak Rao

引用次数: 0

Abstract

Estimating the probability density of a population while preserving the privacy of individuals in that population is an important and challenging problem that has received considerable attention in...

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差异隐私下贝叶斯非参数混合建模的 MCMC

估算一个群体的概率密度，同时保护该群体中个体的隐私，是一个重要而具有挑战性的问题，在...

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来源期刊

Journal of Computational and Graphical Statistics 数学-统计学与概率论

CiteScore

3.50

自引率

8.30%

发文量

153

审稿时长

>12 weeks

期刊介绍： The Journal of Computational and Graphical Statistics (JCGS) presents the very latest techniques on improving and extending the use of computational and graphical methods in statistics and data analysis. Established in 1992, this journal contains cutting-edge research, data, surveys, and more on numerical graphical displays and methods, and perception. Articles are written for readers who have a strong background in statistics but are not necessarily experts in computing. Published in March, June, September, and December.