文章基本信息

标题：Population Synthesis Based on Joint Distribution Inference Without Disaggregate Samples
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作者：Peijun Ye ; Xiaolin Hu ; Yong Yuan 等
期刊名称：Journal of Artificial Societies and Social Simulation
印刷版ISSN：1460-7425
出版年度：2017
卷号：20
期号：4
页码：1-27
DOI：10.18564/jasss.3533
出版社：University of Surrey, Department of Sociology
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.
关键词：Population Synthesis; Sample-Free; Iterative Proportional Fitting