*This Blog entry is from the Netica Basics section in **Learn Netica**.*

Bayesian Methods should be considered as being incompatible with continuous variables as the premise of the analysis technique is that it apportions probability to states (akin to the sides of a dice). Embracing the state only maxim of Bayesian Networks, presented with a continuous variable, the task is to convert that continuous variable into a state.

In the Blog entries thus far there have been several methods presented to bin variables for the purposes of model improvement. Netica provides a quick and convenient means to turn continuous variables into states, a process it refers to as discretisation.

There are three useful automated forms of discretisation offered by Netics:

· Fixed Bin

· Exponential Bin

· Natural Logarithm

The boundaries can be bound by -infinity or infinity if it is felt that the lower or upper bounds may change over time.

To enter the discretisation for a Node, right click on the node, then click properties:

It can be noted that the current node is set as Discrete, which means that States and their values are entered manually:

Click on the button Discrete which will present the opportunity to change the node to be Continuous:

Upon changing the node type to Continuous, click on the Description button which will expose a sub menu, then select Discretisation:

On clicking the Discretisation button, the large textbox will now accept (rather process) the shorthand notation that will divide a continuous variable into states:

Clearing out any existing values, shorthand will be used to specify the lower boundary, the upper boundary and the number of bins between these boundaries, in this example 0 is the lower boundary, 100 is the upper boundary and there are to be 5 bins:

`[0,100] / 5`

Upon clicking OK the node will be updated with these states. If prompted to remove existing states, click OK:

This example uses a Fixed Bin shorthand. There are three types of shorthand available, where the values in highlight are the parameters:

· Fixed Bin (as example): [Begin,End] / Bin

· Exponential Bin: [Begin, End] +%Bigger

· Natural Logarithm: [Begin, End] / L Bin

If the production values of the upper and lower bound are not known at design time, then -infinity or infinity can be used as lower and upper bound respectively. The use of infinity will bring about runtime resizing of the bounds.