# test input file for graph_sampler
# directed acyclic graph (BN) inference with 14 nodes,
# basic edge prior and concordance priors, normal-gamma data model
# =========================================================================

n_runs    = 1E5;
n_burn_in = 1E4;

random_seed = 49740;

n_nodes = 14;

bayesian_network = true;

initial_adjacency = matrix {empty};     

hyper_pB = matrix {equanimous};

concordance_prior = true;            
lambda_concordance = 1.0;                
edge_requirements = matrix
{0, 0, 0, 0, 1, 0, 0, 0,-1,-1,-1,-1,-1,-1,
 0, 0, 0, 0, 1, 0, 0, 0,-1,-1,-1,-1,-1,-1,
 0, 0, 0, 0, 0, 1, 1, 0,-1,-1,-1,-1,-1,-1,
 0, 0, 0, 0, 0, 0, 1, 0,-1,-1,-1,-1,-1,-1,
-1,-1,-1,-1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
-1,-1,-1,-1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
-1,-1,-1,-1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
-1,-1,-1,-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-1,-1,-1,-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-1,-1,-1,-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-1,-1,-1,-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-1,-1,-1,-1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
-1,-1,-1,-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
-1,-1,-1,-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};     

likelihood = normal_gamma;

n_data = 10;

data = matrix { # data vector for each node, one node per line
1.803, 6.634, 3.477, 0.2017, 0.9843, 9.324, 2.254, 8.912, 2.839, 1.676, 
5.263, 8.577, 8.066, 2.6403, 0.5815, 6.019, 7.199, 0.615, 7.087, 4.170, 
2.544, 9.618, 16.68, 1.4963, 3.6925, 14.47, 9.238, 8.708, 15.63, 2.669,
15.25, 22.00, 19.18, 4.2066, 1.6519, 20.94, 15.03, 11.50, 17.03, 11.09,
106.3, 103.8, 163.0, 134.28, 10.351, 109.9, 140.4, 133.3, 131.4, 104.0,
131.6, 143.0, 152.0, 156.23, 13.863, 173.7, 127.3, 155.0, 162.7, 115.8,
806.0, 5524 , 2247 , 228.26, 588.07, 7664 , 2104 , 5408 , 1956 , 789.0,
2292 , 13875, 7581 , 577.53, 1829.7, 22661, 5382 , 19500, 7467 , 2147 ,
302.7, 1423 , 800.6, 62.624, 223.10, 2430 , 555.5, 1787 , 585.8, 203.3,
53.99, 39.43, 50.89, 69.417, 5.7054, 49.79, 63.44, 65.49, 62.16, 55.70,
94.88, 454.4, 224.9, 21.118, 54.863, 621.5, 174.1, 572.8, 275.8, 83.64,
2.570, 7.244, 11.67, 1.4637, 2.0093, 9.707, 5.568, 5.425, 8.426, 1.643,
18.89, 10.24, 19.18, 29.728, 1.9324, 16.59, 22.52, 19.26, 17.48, 17.68,
6.551, 3.803, 7.731, 9.0954, 0.8987, 5.894, 6.802, 7.675, 7.826, 4.673};

save_chain = true;
save_best_graph = true;  
save_edge_probabilies = true;
n_saved_adjacency = 30;

# End.
