摘要：Constructing agent data with detailed information on their sociodemographics is substantially important for
agent-based modelling. However, to collect data about the whole population is not efficient, since it
requires an expensive and time-consuming survey, especially for a large population. The paper uses a
novel approach that integrates Bayesian network (BN) and generalized raking (GR) multilevel iterative
proportional fitting (IPF). Furthermore, the approach is applied to construct the population for Greater
Jakarta, Indonesia, which consists of 30 million inhabitants. The results show that the BN approach can
produce data that represent the probability distribution of sample data and that the IPF can match it
against aggregate census data.
关键词：Bayesian network ; generalized raking ; population synthesis ; agent-based model
