// Bayesian network 
network "InternalNetwork" { //8 variables and 8 probability distributions
}
variable  "I" { //2 values
	type discrete[2] {  "high"  "low" };
	property "position = (62, 100)" ;
}
variable  "R" { //2 values
	type discrete[2] {  "high"  "low" };
	property "position = (302, 103)" ;
}
variable  "M" { //2 values
	type discrete[2] {  "may"  "aug" };
	property "position = (507, 97)" ;
}
variable  "E" { //2 values
	type discrete[2] {  "low"  "high" };
	property "position = (59, 243)" ;
}
variable  "S" { //2 values
	type discrete[2] {  "high"  "low" };
	property "position = (304, 243)" ;
}
variable  "A" { //2 values
	type discrete[2] {  "good"  "bad" };
	property "position = (506, 242)" ;
}
variable  "L" { //2 values
	type discrete[2] {  "yes"  "no" };
	property "position = (305, 379)" ;
}
variable  "J" { //2 values
	type discrete[2] {  "yes"  "no" };
	property "position = (171, 488)" ;
}
probability (  "I" ) { //1 variable(s) and 2 values
	table 
		0.5	// p(high | evidence )
		0.5; 	// p(low | evidence );
}
probability (  "R" ) { //1 variable(s) and 2 values
	table 
		0.8	// p(high | evidence )
		0.2; 	// p(low | evidence );
}
probability (  "M" ) { //1 variable(s) and 2 values
	table 
		0.3	// p(may | evidence )
		0.7; 	// p(aug | evidence );
}
probability (  "E"  "I"  "R" ) { //3 variable(s) and 8 values
	table 
		 0.9 0.5 0.7 0.1 0.1 0.5 0.3 0.9;
}
probability (  "S"  "R" ) { //2 variable(s) and 4 values
	table 
		 0.9 0.7 0.1 0.3;
}
probability (  "A"  "R"  "M" ) { //3 variable(s) and 8 values
	table 
		 0.2 0.5 0.3 0.8 0.8 0.5 0.7 0.2;
}
probability (  "L"  "A"  "S"  "E" ) { //4 variable(s) and 16 values
	table 
		 0.4 0.8 0.5 0.9 0.1 0.5 0.3 0.7 0.6 0.2 0.5 0.1 0.9 0.5 0.7 0.3;
}
probability (  "J"  "E"  "L" ) { //3 variable(s) and 8 values
	table 
		 0.99 0.05 0.9 0.0 0.01 0.95 0.1 1.0;
}