text: **A**, also called the observation model, represents the state-to-observation likelihood model. 
  This describes how observations depend on or are generated by states of the environment. 
  It is structured as a matrix with a column for each possible environmental state s, and a row 
  for each possible observation o. Each column is then a categorical probability distribution 
  over the observations that will occur given the environmental state (meaning that each 
  column must contain non-negative values that sum to 1). If the observations are multidi- 
  mensional (i.e., multiple observations are made at each time point), there is a matrix for 
  each observation modality. If two or more states determine the observation, the likelihood 
  model then becomes a tensor. If**A**is imprecise (i.e., the probabilities are highly entropic and 
  evenly distributed), observations are taken to carry less information about the environment, 
  in many cases leading to more uncertain inferences, and vice versa. 
  **B**, also called the transition model, describes the state-to-state transition probabilities of 
  environmental states s.**B**encodes the agent’s assumptions about how the environment 
  changes over time, depending on its actions. It has a column and a row for each environ- 
  mental state s, where each column is a categorical probability distribution over the states 
  the environment will take on the next time step, given the state it is currently in. If the envi- 
  ronment is modelled as multidimensional, there will be a matrix for each environmental 
  state factor. Additionally, there is a separate matrix for each possible action (making each 
  factor in**B**a tensor). This means that for every factor in the model, there may be one or 
  more actions that pick out the appropriate slice of the tensor. Action therefore allows the 
  agent to predict that the environment (and the corresponding observations) will change 
  differently depending on the actions that it chooses. If**B**is imprecise (i.e., highly entropic), 