# test input file for graph_sampler
# directed acyclic graph (BN) inference with 3 nodes, first 10 iterations
# basic edge prior, dirichlet data model
# =========================================================================

n_runs    = 10;
n_burn_in = 0;

random_seed = 49740;

n_nodes = 3;

bayesian_network = true;

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

hyper_pB = matrix {equanimous};

likelihood = dirichlet;

n_data_levels = array {2, 2, 2};

n_data = 10;

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

save_chain = true;
n_saved_adjacency = 10;
save_best_graph = true;  
save_edge_probabilies = true;

# End.
