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Activity Number: 355 - Advanced Bayesian Topics (Part 4)
Type: Contributed
Date/Time: Thursday, August 12, 2021 : 10:00 AM to 11:50 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #319033
Title: Probabilistic Population Synthesis for Decision-Making
Author(s): Christopher Grubb*
Companies: Virginia Tech
Keywords: synthetic population; bayesian; policy

There is an ever-increasing need for evidence based governance and policy-making. Oftentimes, the data available for such decisions is incomplete and many sources need to be combined. In addition, in order to understand risks, a proper measure of uncertainty about whatever population is desired. One solution to quantifying uncertainty about a population given various different data sources is to use all available information to create synthetic populations. We propose a high-fidelity probabilistic framework for population synthesis as an alternative to deterministic approaches such as iterative proportional fitting. This allows for multiple different synthetic populations to be created, upon which any relevant model can be fit, in order to make decisions. The probabilistic creation of the synthetic populations propagates uncertainty into the populations, resulting in the ability to describe uncertainty. We first explore a synthetic toy example for comparison to other methods, and then provide a working example using real data from Arlington, VA.

Authors who are presenting talks have a * after their name.

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