Bayesian Network Inference in Binary Logistic Regression: A Case Study of Salmonella sp Bacterial Contamination on Vannamei Shrimp

Abstract

Recently binary logistic regression has been used to identify four factors or predictor variables that supposedly influence the response variable, which is testing result of Salmonella sp bacterial contamination on vannamei shrimp. Binary logistic regression analysis results that there are two predictor variables which is significantly affect the testing result of Salmonella sp bacterial contamination on vannamei shrimp, those are the testing result of Salmonella sp bacterial contamination on farmers hand swab and the subdistrict of vannamei shrimp ponds. Those significant predictor variables selected have been modelled in binary logit model. This paper proposes to study the statistical associations between the two significant predictor variables and the contamination of Salmonella sp bacterial on vannamei shrimp and to build a numerical simulation of two significant predictor variables parameters using bayesian network inference. Directed Acyclic Graph (DAG) is applied for modelling binary logit model of significant factors in bayesian network inference.