text: 2.2. Perception in Active Inference 
  
  In AIF, perception is conceptualised as the result of variational (i.e., approximate) 
  Bayesian inference, performed by minimising the VFE to optimise parameters of poste- 
  rior beliefs about the environment. In exact Bayesian inference, we use a parametrised 
  generative model m to make an optimal inference about state s of the environment based 
  on observation o. This is performed by combining a prior belief over states p(s|m); a like- 
  lihood model p(o|s, m); and the model evidence p(o|m), a normalisation term encoding 
  the likelihood of receiving the given observations across all possible environmental states, 
  as follows [1]: 
  
  p(o|s, m)p(s|m) 
  p(o|m) 
  p(s|o, m) =  (1) 
  
  The posterior distribution over states given observations p(s|o, m) here represent the agent’s 
  beliefs about the environment. Forming beliefs in this way is thought to be the process that 
  enables conscious, as well as unconscious, perception. The product of the likelihood model 
  and prior is also called the joint likelihood p(o, s|m), which fully defines the generative 
  model, and which we use henceforth. In the following, for notational simplicity, we also 
  omit denoting the dependency on the generative model m. 