text: Θ is then described by a Dirichlet distribution parametrised by a set of concentration 
  parameters*θ*: 
  
  p(Θ) = Dir(Θ|*θ*)  (19) 
  
  The concentration parameter of a Dirichlet distribution is essentially a non-negative count 
  of how many times the given category (be it a type of observation or state transition) has 
  occurred. The distribution of concentration parameter counts will determine the shape 
  of the estimated categorical probability distribution, while the scale of the concentration 
  parameters will determine the certainty per precision of the belief. Updating beliefs about 
  Θ (the parameters in the matrices) then corresponds to updating these concentration 
  parameters*θ*with the following update equation: 
  
  *θ*t+1 =*ω*∗*θ*t +*η*∗*χ*t 
  (20) 
  
  The updated value for the concentration parameter (*θ*t+1) is found by adding the previous 
  concentration parameter*θ*t multiplied by a forgetting rate*ω*to the observed data count 
  *χ*(either the observation in the case of**A**learning, or the inferred state or state transition 
  for other matrices) multiplied by a learning rate*η*. With this relatively simple update 
  equation—which, in essence, amounts to just counting the occurrences of categories—an 
  AIF agent can update its beliefs about the various matrices it uses to make inferences about 
  environmental states. For more details on parameter learning with POMDPs, see [23,33,52]. 