text: We can now combine our definition of VFE with Equation (4): 
  
  DKL[q(s)∥p(s|o)] = F [q(s), o] + ln p(o) 
  (7) 
  
  Finally, we can reorganise this equation to show that the VFE is the sum of the divergence of 
  the approximation posterior and exact posterior (if we could perform exact inference, this is 
  what we would obtain) and the surprise ℑ = − ln(p(o)) (the negative log model evidence): 
  
  F [q(s), o] = DKL[q(s)∥p(s|o)] 
  (cid:125) 
  − ln p(o) 
  (cid:125) 
  (cid:123)(cid:122) 
  (cid:124) 
  Surprise 
  
  (8) 
  (cid:124) 
  (cid:123)(cid:122) 
  Divergence 
  
  Since the KL divergence is non-negative, the VFE becomes an upper bound on the surprise: 
  
  F [q(s), o] ≥ − ln p(o)  (9) 
  
  By rearranging the parts of this expression, we can express the VFE as a balance between 
  the complexity and accuracy, where the accuracy is how well the model predicts observa- 
  tion, and the complexity is how much the beliefs need to change in order to maintain a 
  high accuracy: 