--- /dev/null
+#include <dai/factorgraph.h>
+#include <iostream>
+
+using namespace std;
+using namespace dai;
+
+int main() {
+ // This example program illustrates how to construct a factorgraph
+ // by means of the sprinkler network example discussed at
+ // http://www.cs.ubc.ca/~murphyk/Bayes/bnintro.html
+
+ Var C(0, 2); // Define binary variable Cloudy (with label 0)
+ Var S(1, 2); // Define binary variable Sprinkler (with label 1)
+ Var R(2, 2); // Define binary variable Rain (with label 2)
+ Var W(3, 2); // Define binary variable Wetgrass (with label 3)
+
+ // Define probability distribution for C
+ Factor P_C( C );
+ P_C[0] = 0.5; // C = 0
+ P_C[1] = 0.5; // C = 1
+
+ // Define conditional probability of S given C
+ Factor P_S_given_C( VarSet( S, C ) );
+ P_S_given_C[0] = 0.5; // C = 0, S = 0
+ P_S_given_C[1] = 0.9; // C = 1, S = 0
+ P_S_given_C[2] = 0.5; // C = 0, S = 1
+ P_S_given_C[3] = 0.1; // C = 1, S = 1
+
+ // Define conditional probability of R given C
+ Factor P_R_given_C( VarSet( R, C ) );
+ P_R_given_C[0] = 0.8; // C = 0, R = 0
+ P_R_given_C[1] = 0.2; // C = 1, R = 0
+ P_R_given_C[2] = 0.2; // C = 0, R = 1
+ P_R_given_C[3] = 0.8; // C = 1, R = 1
+
+ // Define conditional probability of W given S and R
+ Factor P_W_given_S_R( VarSet( S, R ) | W );
+ P_W_given_S_R[0] = 1.0; // S = 0, R = 0, W = 0
+ P_W_given_S_R[1] = 0.1; // S = 1, R = 0, W = 0
+ P_W_given_S_R[2] = 0.1; // S = 0, R = 1, W = 0
+ P_W_given_S_R[3] = 0.01; // S = 1, R = 1, W = 0
+ P_W_given_S_R[4] = 0.0; // S = 0, R = 0, W = 1
+ P_W_given_S_R[5] = 0.9; // S = 1, R = 0, W = 1
+ P_W_given_S_R[6] = 0.9; // S = 0, R = 1, W = 1
+ P_W_given_S_R[7] = 0.99; // S = 1, R = 1, W = 1
+
+ // Build factor graph consisting of those four factors
+ vector<Factor> SprinklerFactors;
+ SprinklerFactors.push_back( P_C );
+ SprinklerFactors.push_back( P_R_given_C );
+ SprinklerFactors.push_back( P_S_given_C );
+ SprinklerFactors.push_back( P_W_given_S_R );
+ FactorGraph SprinklerNetwork( SprinklerFactors );
+
+ // Write factorgraph to a file
+ SprinklerNetwork.WriteToFile( "sprinkler.fg" );
+
+ // Reread the factorgraph from the file
+ SprinklerNetwork.ReadFromFile( "sprinkler.fg" );
+
+ // Output some information about the factorgraph
+ cout << SprinklerNetwork.nrVars() << " variables" << endl;
+ cout << SprinklerNetwork.nrFactors() << " factors" << endl;
+
+ // Calculate joint probability of all four variables
+ Factor P;
+ for( size_t I = 0; I < SprinklerNetwork.nrFactors(); I++ ) {
+ P *= SprinklerNetwork.factor( I );
+ }
+ // P.normalize(); // Not necessary: a Bayesian network is already normalized by definition
+
+ // Calculate some probabilities
+ double denom = P.marginal( W )[1];
+ cout << "P(W=1) = " << denom << endl;
+ cout << "P(S=1 | W=1) = " << P.marginal( VarSet( S, W ) )[3] / denom << endl;
+ cout << "P(R=1 | W=1) = " << P.marginal( VarSet( R, W ) )[3] / denom << endl;
+
+ return 0;
+}