# sample input file for graph_sampler
# =========================================================================

nNodes = 3;
seed = 49740;

initial_adjacency = matrix
{0, 1, 0,
 0, 0, 0,
 1, 0, 0};

hyper_pB = matrix
{0.0, 0.5, 0.5,
 0.5, 0.0, 0.5,
 0.5, 0.5, 0.0};

bBN = true;
#dynamic_bayesian_network = true;
autocycle = false;
bsave_the_chain = true;
bsave_best_graph = true;
bsave_the_edge_probabilies = true;
bsave_the_degree_counts = true;
bsave_the_motifs_probabilies = true;
n_saved_adjacency = 1;

nRuns = 10;
nBurnin = 0;

bPriorConcordance = true;
lambda_concord = 1.0;
edge_requirements = matrix
{-1,-1,-1,
  1,-1,-1,
  1,-1,-1};

bPriorDegreeNode = false;
#gamma_degree = 1.7;

bPriorMotif  = false;
#alpha_motif = 2;
#beta_motif  = 50;

zellner_score = true;
gamma_zellner = 10;

#dirichlet_score = true;

nData = 4;

data = matrix {
1.1, 1.3, 1.4, 1.35,
2.1, 2.4, 2.5, 2.45,
3.4, 3.6, 3.8, 3.85};

# End.
