text: 
  """
  1) The conditional probability distribution is using a measurement to restrict the likely value of
     one of the variables. If there is correlation, this will also affect what we know (conditionally)
     about the other! However, the marginal probability *only* depends on the direction along
     which we are marginalizing. So, when the conditional probability is based on a measurement at the
     means, it is the same as marginalization, as there is no additional information. A further note
     is that we can also marginalize along other directions (e.g. a diagonal), but we are not exploring
     this here.
  
  2) The larger the correlation, the more shared information. So the more we gain about the
     second variable (or hidden state) by measuring a value from the other.
  
  3) The variable (hidden state) with the lower variance will produce a narrower
     conditional probabilty for the other variable! As you shift the correlation, you will see
     small changes in the variable with the low variance shifting the conditional mean of the
    variable with the large variance! (So, if X has low variance, changing CY has a big effect.)
  """