Population Synthesis Based on Joint Distribution Inference Without Disaggregate Samples
文献类型：期刊论文
| 作者 | Ye, Peijun1,2 ; Hu, Xiaolin3; Yuan, Yong1,2; Wang, Fei-Yue1,2,4
|
| 刊名 | JASSS-THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION
 |
| 出版日期 | 2017-10-31 |
| 卷号 | 20期号:4页码:16 |
| 关键词 | Population Synthesis Sample-free Iterative Proportional Fitting |
| DOI | 10.18564/jasss.3533 |
| 文献子类 | Article |
| 英文摘要 | Synthetic population is a fundamental input to dynamic micro-simulation in social applications. Based on the review of current major approaches, this paper presents a new sample-free synthesis method by inferring joint distribution of the total target population. Convergence of multivariate Iterative Proportional Fitting used in our method is also proved theoretically. The method, together with other major ones, is applied to generate a nationwide synthetic population database of China by using its overall cross-classification tables as well as a sample from census. Marginal and partial joint distribution consistencies of each database are compared and evaluated quantitatively. Final results manifest sample-based methods have better performances on marginal indicators while the sample-free ones match partial distributions more precisely. Among the five methods, our proposed method can significantly reduce the computational cost for generating synthetic population in large scale. An open source implementation of the population synthesizer based on C# used in this research is available at https://github.com/PeijunYe/PopulationSynthesis.git. |
| WOS关键词 | PROPORTIONAL FITTING PROCEDURE ; CONTINGENCY-TABLES ; GENERATION ; MICROSIMULATION ; CONVERGENCE ; SIMBRITAIN ; MATRICES ; MARGINS |
| WOS研究方向 | Social Sciences - Other Topics |
| 语种 | 英语 |
| WOS记录号 | WOS:000416164100018 |
| 资助机构 | National Natural Science Foundation of China(61603381 ; Department of Energy, USA(4000152851) ; 71472174 ; 61533019 ; 71232006) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/20084]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队 |
| 作者单位 | 1.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China 2.Qingdao Acad Intelligent Ind, Qingdao, Peoples R China 3.Georgia State Univ, Dept Comp Sci, 25 Pk Pl, Atlanta, GA 30084 USA 4.Natl Univ Def & Technol, Mil Computat Expt & Parallel Syst Res Ctr, Changsha, Hunan, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ye, Peijun,Hu, Xiaolin,Yuan, Yong,et al. Population Synthesis Based on Joint Distribution Inference Without Disaggregate Samples[J]. JASSS-THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION,2017,20(4):16. |
| APA | Ye, Peijun,Hu, Xiaolin,Yuan, Yong,&Wang, Fei-Yue.(2017).Population Synthesis Based on Joint Distribution Inference Without Disaggregate Samples.JASSS-THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION,20(4),16. |
| MLA | Ye, Peijun,et al."Population Synthesis Based on Joint Distribution Inference Without Disaggregate Samples".JASSS-THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION 20.4(2017):16. |
入库方式： OAI收割
来源：自动化研究所
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。