asymptoticpropertiesofdistributedsocialsamplingalgorithm
文献类型:期刊论文
作者 | Liu Qian2; He Xingkang1; Fang Haitao2 |
刊名 | sciencechinainformationscience
![]() |
出版日期 | 2020 |
卷号 | 63期号:1 |
ISSN号 | 1674-733X |
英文摘要 | Social sampling is a novel randomized message passing protocol inspired by social communication for opinion formation in social networks. In a typical social sampling algorithm, each agent holds a sample from the empirical distribution of social opinions at initial time, and it collaborates with other agents in a distributed manner to estimate the initial empirical distribution by randomly sampling a message from current distribution estimate. In this paper, we focus on analyzing the theoretical properties of the distributed social sampling algorithm over random networks. First, we provide a framework based on stochastic approximation to study the asymptotic properties of the algorithm. Then, under mild conditions, we prove that the estimates of all agents converge to a common random distribution, which is composed of the initial empirical distribution and the accumulation of quantized error. Besides, by tuning algorithm parameters, we prove the strong consistency, namely, the distribution estimates of agents almost surely converge to the initial empirical distribution. Furthermore, the asymptotic normality of estimation error generated by distributed social sample algorithm is addressed. Finally, we provide a numerical simulation to validate the theoretical results of this paper. |
语种 | 英语 |
CSCD记录号 | CSCD:6655421 |
源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/51274] ![]() |
专题 | 中国科学院数学与系统科学研究院 |
作者单位 | 1.皇家工学院 2.中国科学院数学与系统科学研究院 |
推荐引用方式 GB/T 7714 | Liu Qian,He Xingkang,Fang Haitao. asymptoticpropertiesofdistributedsocialsamplingalgorithm[J]. sciencechinainformationscience,2020,63(1). |
APA | Liu Qian,He Xingkang,&Fang Haitao.(2020).asymptoticpropertiesofdistributedsocialsamplingalgorithm.sciencechinainformationscience,63(1). |
MLA | Liu Qian,et al."asymptoticpropertiesofdistributedsocialsamplingalgorithm".sciencechinainformationscience 63.1(2020). |
入库方式: OAI收割
来源:数学与系统科学研究院
浏览0
下载0
收藏0
其他版本
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。