text: *∂*Ft 
  *∂*q(s f 
  t ) 
  
  Solving for q(s f 
  t ) yields 
  
  (cid:18) 
  (cid:105)(cid:19) 
  ln q(s f 
  t ) = E 
  E 
  qi\ f [ln P(ot | st)] + ln 
  − 1 
  f 
  t−1,u  f 
  t−1) 
  P(s 
  
  which leads us to the coordinate descent update equation: 
  
  (cid:18)  (cid:18) 
  (cid:104) 
  q∗(s f  P(s f 
  t | s f  t−1, u f 
  E  E 
  t ) =*σ* qi\ f [ln P(ot | st)] + ln  t−1) 
  f 
  t−1,u  f 
  t−1) 
  P(s 
  
  where E 
  qi\ f denotes the expectation over q(s) for factor i, where the posterior over states 
  in the other factors f are kept constant. By iteratively solving (16), the FPI scheme will 
  eventually find a local optimum and converge to a solution for the variational posterior. 
  By default, ActiveInference uses 10 iterations or stops when*∂*Ft < 0.001. This posterior 
  then comprises the AIF agent’s belief about the state of the environment, and therefore, 
  its perception. 