# test input file for graph_sampler
# directed acyclic graph generation with 4 node, with a tuned degree prior
# and expressions here and there.
# =========================================================================

n_runs    = 100 * 100; # expression
n_burn_in = 100 + 900; # expression

random_generator = mersenne_twister;

random_seed = 10 * (4973 + 1); # expression

n_nodes = 4;

bayesian_network = true;

initial_adjacency = matrix {empty};

hyper_pB = matrix {0,   0.1, 0.2, (0.01 * 10), # expression for matrix element
                   0.9, 0,   0.1, 0.1,
                   0.1, 0.1, 0,   0.1,
                   0.9, 0.1, 0.1, 0};

degree_prior = true;
gamma_degree = 5.0;

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

# End.
