Types of Variables in Bayesian Networks
Two types of variables can be used for the description of all nodes in a Bayesian Network. These types are:
• Discrete variables: This type is appropriate for nodes where the answer can be selected from a small set of discrete choices. For each possible choice, a confidence measure is attached to the node.
• Continuous variables: This type suits nodes where continuous domain measurements are to be taken. For such nodes, a conditional probability distribution is attached to the node so that the continuous measurement received at the input can be converted into a real number representing a probability measurement of the node.
A Bayesian Network that contains both discrete and continuous nodes is called Hybrid or Mixed Bayesian Network.
• Discrete variables: This type is appropriate for nodes where the answer can be selected from a small set of discrete choices. For each possible choice, a confidence measure is attached to the node.
• Continuous variables: This type suits nodes where continuous domain measurements are to be taken. For such nodes, a conditional probability distribution is attached to the node so that the continuous measurement received at the input can be converted into a real number representing a probability measurement of the node.
A Bayesian Network that contains both discrete and continuous nodes is called Hybrid or Mixed Bayesian Network.