Bayesian network in providing domain knowledge is overcome
Abstract
A Bayesian network is a probabilistic inference model that is useful for commercial decisions such as product development. Multiple events are shown as oval nodes, with edges indicating their connections. However, in order to achieve a significant effect, domain knowledge must be effectively configured; for example, more consumer response to the product leads to greater clarity of product needs. This domain knowledge is set up as an edge linking nodes. However, in some circumstances, the structural restriction in a Bayesian network forbids this design. The authors offer a strategy to bypass this restriction in this study by inserting redundant factor nodes obtained by applying factor analysis to domain knowledge data.
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