text: (cid:7) 
  **using**Distributions 
  
  # Set priors 
  parameter_priors = Dict ( 
  " alpha " => Truncated ( Normal (5 ,5 ), 0 , 20 ) 
  
  ) 
  (cid:6)  (cid:5) 
  
  Next, we instantiated the probabilistic model with data and parameter priors: 
  (cid:7) 
  model = create_model ( 
  
  agent , # ActionModels Agent containing the model 
  parameter_priors , # Dictionary with parameter priors 
  data ; # Dataframe containing data from all subjects 
  grouping_cols = [: SubjectID ], # Identifier column 
  input_cols = [" Location ", " Reward ", " Cue "], # Observation columns 
  action_cols = [" Action_Location ", " Action_Reward "] # Columns containing actions 
  
  ) 
  (cid:6) 
  
  Finally, we used the fit_model function to perform (parallelised) parameter estimation for 
  each of the agents: 
  (cid:7)  (cid:4) 
  results = fit_model ( 
  
  model ; # Model object 
  parallelization = MCMCDistributed () # Parallelize over chains 
  n_iterations = 1000 , # Number of iterations 
  n_chains = 4 # Number of chains 
  
  ) 
  (cid:6)  (cid:5) 