# Section for paths to data
data:
    path_data_state: data/data_state_sim_nProteins=1100.txt
    path_mapping_peptides: data/mapping_peptides_sim_nProteins=1100.txt
    sep_input: " "

# Section for prior parameters
priors:
    # Random censoring probability
    p_rnd_cen:
        prior_a: 1.
        prior_b: 1.
    # Hyperparameters for n_states model
    n_states_dist:
        prior_a: 1.
        prior_b: 1.
        prior_mean_log: 2.65
        prior_prec_log: 1.
    # Hyperparameters for state-level variance distribution:
    sigmasq_dist:
        prior_shape: 1.
        prior_rate: 1.
        prior_mean_log: 2.65
        prior_prec_log: 2.35
    # Hyperparameters for peptide-level variance distribution
    tausq_dist:
        prior_shape: 1.
        prior_rate: 1.
        prior_mean_log: 2.65
        prior_prec_log: 2.35
    # Protein-level means
    mu:
        prior_mean: 0.
        prior_prec: 0.

# Section for initializations
init:
    # Probability of random censoring
    p_rnd_cen: 0.1
    # Coefficients for intensity-based censoring model
    eta:
        mean: [-2.5, 0.5]
        sd: [0., 0.]
        cor: 0.
    # Hyperparameters for state-level variance distribution:
    sigmasq_dist:
        shape: 4.
        rate: 2.
    # Hyperparameters for peptide-level variance distribution
    tausq_dist:
        shape: 4.
        rate: 2.
    
# Section for algorithmic settings for MCMC
settings:
    n_iterations: 100
    propDf: 3.
    verbose: 1
    verbose_interval: 5


Add YAML configuration for large (1100 protein) simulated dataset.
Add YAML configuration for large (1100 protein) simulated dataset.
