bnet Cancer { 
// Networks properties. 
        title="Red bayesiana para el diagnostico de cancer 
               metastasico";
        comment="Es el ejemplo mas famoso de red bayesiana";
        author="Greg Cooper";
        whochanged="Equipo Proyecto Elvira";
        whenchanged="22/04/98";
// By default, locked=false;
// By default, version=1.0;
        default node states=(ausente presente);
// instead of "(absent present)"

// Nodos

node Cancer {
// By default, belong to finite-states class.  
        title="Cancer metastasico";
        comment="Indica si se da la enfermedad o no";
// By default, states=(ausente presente)  
 }

node Calcio {
        title="Elevacion del calcio serico";
        states=(normal elevado);
 }

node Tumor {
        title="Tumor cerebral";
        states = (presente ausente);
 }

node Coma;
// [By default, "title" is the same that "identifier".]}  

node Jaquecas;

// Links
link Cancer Calcio; 
// By default, is a directed link.  
link Cancer Tumor; 
link Calcio Coma; 
link Tumor Coma; 
link Tumor Jaquecas;

// Relaciones

relation Cancer { 
// "Cancer" {\it indica cuales son los nodos que intervienen.}  
         comment = "Prevalencia del cancer metastasico"; 
// By default, the relation is active.  
// By default, is a conditional probability.  
  
         values=convex-set(
                          table (0.2 0.8)
                          table (0.3 0.7)
                          );
}

relation Calcio Cancer { 
        values=table ([elevado,presente]=0.8,
                [elevado,ausente]=0.2,
                [normal,presente]=0.2,
                [normal,ausente]=0.8);
 }

relation Tumor Cancer { 
        values=table ([0,presente]=0.2,
                [0,ausente]=0.05,
                [1,presente]=0.8,
                [1,ausente]=0.95);
 }

relation Jaquecas Tumor { 
        values=table (0.8, 0.6,0.2,0.4);
 }

relation Coma Calcio Tumor {
        comment="Tanto la elevacion del calcio serico como el tumor
                 cerebral pueden producir coma";
        values=tree (
               case  Coma{
                  presente=case  Calcio{
                            elevado=0.8;
                            normal=case  Tumor{
                                     presente=0.8;
                                     ausente=0.05;
                                   }
                           }
                  ausente= case  Calcio{
                            elevado=0.2;
                            normal=case  Tumor{
                                     presente=0.2;
                                     ausente=0.95;
                                   }
                           }
               }
       );
 }
} // End network.