Structurally aware discretisation for Bayesian networks

Bayesian networks represent a versatile probabilistic modelling technique widely used to tackle a range of problems in many different domains. However, they are discrete models, and a significant decision when designing a BN is how to split the continuous variables into discrete bins. Default options offered in most BN packages include assigning an equal number of cases to each bin or assigning equal sized bins. However, these methods discretise nodes independently of each other. When learning probabilities from data, this can result in conditional probability tables (CPTs) with missing or uninformed probabilities because data for particular bin combinations (scenarios) is either missing or scarce. This can result in poor model performance