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    PM



    
        Bayes Parametric Model 
    



    Description of Model     
Bayes Parametric Model (Bayes PM) takes a DAG and adds to it two bits of information:

    For each named node in the graph, the number of categories for the variable by that name.
    For each variable, with a given number of categories, the list of category names for that variable.
    

Given the graph and the additional information in (1) and (2), a Bayes net can be formally specified; it is
    determined what all the parameters of the Bayes net are, although no values for parameters are yet known. To specify
    a Bayes net up to parameter values, a Bayes Instantiated Model must be constructed, based on a Bayes PM. For details
    on the parameters of a Bayes IM, see Bayes Instantiated Model.
It is assumed in the current version of Tetrad that all discrete variables are nominal--that is, that the order of
    their categories is not important. See Defining
        Discrete Variables for more details. 


    How to Construct a Bayes PM
For example, say you put the following boxes on the session, connected as follows:

For example, say you start with this DAG. (It need not be, specifically, in a Directed Acyclic Graph box; all that
    matters is that it contain only directed edges with no cycles.)

If you click "Save" and double click the PM1 box, you are given a choice of which model type you would like
    to construct. Choose "Bayes Parametric Model."

Once you click OK, the following dialog appears:

In this dialog, you can click on a variable and edit its number of nodes and category names. For instance, we can
    change the number of categories for X1 to 3 and set its categories to <Low, Medium, High>.

When you're finished editing categories for variables, click "Save." 


Potential Parents for Bayes Parametric Model 
The Bayes PM can take any graph as parent that contains a DAG--that is, a graph that contains only directed edges (-->)
    with no cycles (i.e. there is no X such that X-->...-->X in the graph). The simplest option is to construct
    Directed Acyclic Graph in the Graph box. (See Directed Acyclic Graph for more
    details.) If the parent is not a DAG, an error message will be displayed when the Bayes PM is constructed.