text: There are many ways in which ActiveInference can be improved. It would be useful 
  to extend the set of dynamic belief states to include prediction errors since they are often 
  used for model-based neuroscience. This would entail departing from discrete state-space 
  (i.e., POMDP) models to consider continuous state-space models apt for Bayesian filtering 
  or predictive coding (see below). An alternative would be to generate prediction errors 
  from belief updating under discrete models, where prediction errors can be read as the 
  (KL) divergence between posterior and prior beliefs (i.e., complexity or information gain). 
  A simple interface could be added for creating custom parametrisations of the requisite 
  parameters that could be parametrised with Boltzmann or Gibbs distributions, as opposed 
  to Dirichlet distributions. Parameter learning could be extended to all generative model 
  parameters, as well as in parametrised forms (e.g., so that the Boltzmann parameter or 
  temperature of the parameters that are learned); similarly for the precision over expected 
  free energies*γ*. Preference priors should also be implementable for environmental states, 
  in addition to observations, and**A**can be made action dependent. 