text: The output of the fit_model function is an object containing the standard Turing chains, 
  which we can use to access the chain statistics: 
  (cid:7)  (cid:4) 
  
  # Extract the Chains object 
  chains = results . chains 
  
  # Rename the chains for interpretable parameter names 
  rename_chains ( chains , model ) 
  (cid:6)  (cid:5) 
  
  ActionModels provides a range of convenience functions for behavioural modelling. We can 
  extract the posterior parameter estimates for each participant, and extract it in a convenient 
  data frame structure for later processing: 
  (cid:7)  (cid:4) 
  
  # Extract quantities from the chain 
  agent_parameters = extract_quantities ( single_subject_model , chains ) 
  # Get posterior median 
  estimates = get_estimates ( agent_parameters ) 
  (cid:6)  (cid:5) 
  
  We can also sample parameter values from the prior and plot the posteriors against the priors: 
  (cid:7)  (cid:4) 
  
  # Sample from the prior 
  prior_chains = sample ( single_subject_model , Prior (), 1000 ) 
  
  # Plot parameter distribution . 
  plot_parameter_distribution ( chains , prior_chains ) 
  (cid:6)  (cid:5) 
  
  See the documentation for ActionModels at github.com/ilabcode/ActionModels.jl for vari- 
  ous other functionalities, including modelling how parameters vary across a population, 