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Free keywords:
Computer Science, Databases, cs.DB
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 on US census datasets, and demonstrate its
feasibility using an epidemic simulation application.
