This is a probabilistic model presented in a graphical way by using Directed Acyclic Diagrams to study the probabilistic relationships among the nodes that make up the network. Each node represent a random variable.
The current version only accepts discrete nodes.
Provide the property values required to define the node as follows:
|Sdescription||Description of the object|
|Sname||Name of the node|
|CSparents||Cell array of parent nodes|
|CSchildren||Cell array of children nodes|
|CPD||Conditional probability table|
|Chyperparameters||Cell of intervals (hyperparameters) SAME SIZE OF CPD|
|Cmapping||Input mapping for the interval random variables|
|Nsize||Size of the node|
|Lroot =false||Flag for root nodes|
|Lboolean= false||Flag for Boolean nodes (only 2 possible outcomes: true and false)|
|evidence||Value of the evidence introduced|
|Vvalues||Values assumed by the parameter (only for discrete nodes spouses of non-discrete nodes)|
|Vbounds||Vector of bounds of discrete nodes|
|censoring=||value of censoring: in case of construction of UserDefRV the realizations equal or below the censoring value are excluded from the distribution (conditional distribution)|
|MimportantDirection||Important direction for the extreme case computation: N.B. each row is associated to a state of the node|
Name the node with only one word, e.g., 'Rain'. If it is necessary to use more than one word, separate each word with the underscore sign "_", e.g., 'Wet_Grass'. Forgetting using the underscore sign to separate words might lead to errors when executing the code.