// Bayesian network 
network "InternalNetwork" { //5 variables and 5 probability distributions
}
variable  "Box" { //3 values
	type discrete[3] {  "GG"  "GS"  "SS" };
	property "position = (86, 26)" ;
}
variable  "Obs_1" { //2 values
	type discrete[2] {  "G"  "S" };
	property "position = (34, 128)" ;
}
variable  "Obs_2" { //2 values
	type discrete[2] {  "G"  "S" };
	property "position = (145, 128)" ;
}
variable  "Rep_1" { //2 values
	type discrete[2] {  "G"  "S" };
	property "observed G" ;
	property "position = (34, 227)" ;
}
variable  "Rep_2" { //2 values
	type discrete[2] {  "G"  "S" };
	property "observed G" ;
	property "position = (144, 227)" ;
}
probability (  "Box" ) { //1 variable(s) and 3 values
	table 
		0.33333333333333331	// p(GG | evidence )
		0.33333333333333331	// p(GS | evidence )
		0.33333333333333331; 	// p(SS | evidence );
}
probability (  "Obs_1"  "Box" ) { //2 variable(s) and 6 values
	table 
		 1.0 0.5 0.0 0.0 0.5 1.0;
}
probability (  "Obs_2"  "Box"  "Obs_1" ) { //3 variable(s) and 12 values
	table 
		 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0;
}
probability (  "Rep_1"  "Obs_1" ) { //2 variable(s) and 4 values
	table 
		 0.69999999999999996 0.20000000000000001 0.29999999999999999 0.80000000000000004;
}
probability (  "Rep_2"  "Obs_2" ) { //2 variable(s) and 4 values
	table 
		 0.75 0.10000000000000001 0.25 0.90000000000000002;
}