<![CDATA[Understanding relationships among products in retail transactions is essential for supporting strategic decisions such as product placement and cross-selling. However, traditional market basket analysis is limited to pairwise associations and cannot capture complex conditional dependencies among multiple items. This study addresses these limitations by applying a Bayesian Network framework to model product relationships probabilistically. Transaction data collected over one month were transformed into a binary dataset of 441 transactions across 31 product categories. The network structure was learned using a stochastic search algorithm with a Bayesian Dirichlet equivalent (BDe) score, enhanced by a multi-seed strategy and edge support aggregation to ensure robustness. Parameters were estimated using maximum likelihood. The results indicate that relationships are both directional and context-dependent, with snack probabilities increasing with instant noodles but decreasing when cigarettes are also present. The LOIO accuracy is 0.6659.]]>
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