# test input file for graph_sampler
# directed acyclic graph (BN) inference with 3 nodes,
# basic edge prior and concordance prior, normal-gamma data model,
# and missing data
# =========================================================================

n_runs    = 1E5;
n_burn_in = 1E4;

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};

concordance_prior = true;            
lambda_concordance = 1.0;                
edge_requirements = matrix           
{-1,-1,-1,
  1,-1,-1,
  1,-1,-1};     

likelihood = normal_gamma;

n_data = 4;

n_data_levels = array {2, 2, 2};

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

save_chain = true;
n_saved_adjacency = 20;
save_best_graph = true;  
save_edge_probabilies = true;

# End.
