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BayesiaLab requires the discretization of the continuous variables. This process basically consists in creating a clone of the hidden continuous variable, with discrete states, usually called bins in this context. Discretization has obviously a huge impact on the model because it defines the perception of the domain.

One of the most important parameters of discretization is the number of bins. It has indeed a direct impact on the model complexity. The more bins there are, the larger the (conditional) probability tables are.

Besides the choice of the thresholds that define the bins, the other important criterion is the number of bins. 

The fourth data import wizard