Hao Wu, Yue Ning, Prithwish Chakraborty, Jilles Vreeken, Nikolaj Tatti, Naren Ramakrishnan

Abstract

Modern studies of societal phenomena rely on the availability of large datasets capturing attributes and activities of synthetic, city-level, populations. For instance, in epidemiology, synthetic population datasets are necessary to study disease propagation and intervention measures before implementation. In social science, synthetic population datasets are needed to understand how policy decisions might affect preferences and behaviors of individuals. In public health, synthetic population datasets are necessary to capture diagnostic and procedural characteristics of patient records without violating confidentialities of individuals. To generate such datasets over a large set of categorical variables, we propose the use of the maximum entropy principle to formalize a generative model such that in a statistically well-founded way we can optimally utilize given prior information about the data, and are unbiased otherwise. An efficient inference algorithm is designed to estimate the maximum entropy model, and we demonstrate how our approach is adept at estimating underlying data distributions. We evaluate this approach against both simulated data and US census datasets, and demonstrate its feasibility using an epidemic simulation application.

People

Yue Ning


Hao Wu


Naren Ramakrishnan


Publication Details

Date of publication:
April 16, 2018
Journal:
ACM Transactions on Knowledge Discovery from Data (TKDD)
Page number(s):
1-22
Volume:
12
Issue Number:
4
Publication note:

Hao Wu, Yue Ning, Prithwish Chakraborty, Jilles Vreeken, Nikolaj Tatti, Naren Ramakrishnan: Generating Realistic Synthetic Population Datasets. ACM Trans. Knowl. Discov. Data 12(4): 45:1-45:22 (2018)