text: Simulations are useful for a variety of purposes, like exploring the consequences of 
  different priors and parameters and establishing the face validity of hypothetical mech- 
  anisms underlying behavioural phenomena. However, we often want to use models to 
  make inferences about specific observed phenomena, like the differences in behaviour 
  between various populations, as in computational psychiatry [14]. One standard method 
  here is model fitting, where we estimate the parameter values (e.g., prior beliefs) of an AIF 
  model that are the most likely given some observed behaviour of a participant. This is often 
  performed with approximate Bayesian methods. In the cognitive and behavioural sciences, 
  the predominant method is Markov Chain Monte Carlo (MCMC) methods [34], which are 
  slower but in the limit can estimate parameter posteriors without making assumptions 
  about their functional form. An alternative, which is more often used in other fields and 
  also available in ActiveInference is variational methods, which are faster but require mak- 
  ing assumptions about the functional form of the posterior. In general, MCMC methods 
  are favourable when making parameter inferences (i.e., comparing parameters of the same 
  model fitted to different data, like two groups of subjects). When performing a Bayesian 
  model comparison (i.e., comparing different models fitted to the same data), the different 
  approaches rely on different approximations of the model evidence, with the variational 