Bayesian network modeling applied to food risks: Data from General Administration of Customs of China as an example - Xuanyu Ying; Anni Lu; Qiang Cai; Jun Lu (Applied Food Research)
We introduce a multidimensional Bayesian Network (BN) modeling approach as an alternative to the classical multivariate regression approach commonly used for risk factor analysis. BN modeling is a rich and flexible analytical technique capable of elucidating complex food data. It is a data-driven graphical modeling technique and exploratory tool that visually presents dependencies and causal associations while retaining statistical rigor in holistic level inference. We used data on monthly unauthorized food imports to China for demonstrating a full Additive BN model in the open-source R language with all code necessary to reproduce our analyses. Compared to the classical regression model, the Additive BN model of this study described more relationships between variables, and overcome the problems of classical discretization of general BN. The analysis showed that food category and import weight were two actionable drivers directly related to risk and can be controlled to manage risk. The risk was also related to where the food is produced, but not where it is likely to be sold. The BN model also is able to systematically elaborate uncertainty and obtain information through probabilistic inference, which might be useful for giving additional new insights potentially not captured by classical methods. Finally, we discuss the potential and evolution of BN, such as dynamic BN.