text: 2.1. POMDPs in Active Inference 
  
  In AIF, the POMDP is one of the most common families of generative models used 
  to make inferences about the environment. It is a Markovian discrete state-space model, 
  where employing it means representing the environment and observations as inhabiting 
  one among a set of possible (possibly multidimensional) states, and that the changes 
  in these states can only depend on the system’s previous state and the agent’s actions. 
  Environmental states are not directly observable, so they have to be inferred based on 
  incoming sensory observations. In AIF for POMDPs and other generative models in general, 
  both perception and action are cast as Bayesian inferences (see Sections 2.2 and 2.3), as well 
  as the learning of parameters of the generative model (see Section 2.4). Crucially, an agent’s 
  generative model does not a priori have to be isomorphic to the true environment (i.e., 
  the data-generating process), although this will generally lead to a successful inference, 
  and that the generative model will therefore often come to resemble the environment 
  through learning. 
  
  A discrete state-space POMDP in AIF is conventionally defined by five main sets of 
  parameters:**A**,**B**,**C**,**D**and**E**[1,33], see Figure 1. Together, these parametrise the agent’s 
  prior beliefs about the prior probability of different states in the environment, how states 
  of the environment change and how they generate observations. Typically, they will be 
  vectors, matrices or tensors; however, henceforth we denote them by their corresponding 
  letter in bold. These make up the components needed for the agent to perform AIF. 