Applying Bayesian Network Models to Fuse Information from Different Data Sources

Applying Bayesian Network Models to Fuse Information from Different Data Sources

Presented at the 7th Annual BayesiaLab Conference at the North Carolina Biotechnology Center.


Successful product innovation relies heavily on multiple types of product tests. These tests include a virtual online concept test at an earlier stage, a blind and identified usage test of prototype products in the middle, and a household panel survey when the product is in the market. Currently, most data analytics are conducted based on separate analyses of sporadic and piecemeal data from different tests. The product development decisions were often made through team meetings to qualitatively summarize isolated analytics of different product tests. We developed a Bayesian framework based on Bayesian Belief Network (BBN) model to systematically aggregate data from different tests and fuse information quantitatively from different sources for better product innovation and consumer understanding. The developed methods can be used either to identify “Body of Evidence” from all available data sources or to conduct cross-inference from one data source to another data source.

Presentation Video

Presentation Slides

About the Presenter

Yong Zhang, Ph.D., Senior Scientist, Procter & Gamble

Dr. Yong Zhang leverages Bayesian data and modeling science to develop a strategy for product design, manufacturing, storage, and transportation across P&G to improve consumers’ quality of life and drive positive influence on the environment and society under different climate change scenarios. He develops modeling and simulation methods and tools through Front End Innovation projects to enable and promote the capability across P&G for breakthrough consumer understanding and product innovation. The methods and tools can be used to extract and integrate information from a variety of data sources to find a “Body of Evidence” for consumer and product research based on Nonparametric Bayesian statistics and deep learning algorithms.\

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