anchor.diff:  
   === Example 4: Shannon Entropy
   Once you have a distribution, you can do lots of fun things with it. In this
  -example, we will compute the shannon entropy of a time series of observed
  +example, we will compute the Shannon entropy of a time series of observed
   events.
   [source,c]
   ----
  
positive.code: = Empirical Distributions
  
  The link:index.html#inform_dist[`inform_dist`] struct provides an _emperical_
  distribution, i.e. histogram, representing the observed frequencies of some
  fixed-sized set of events. This class is the basis for all of the fundamental
  information measures provided on discrete probability distributions.
  
negative.code: ----
  
  === Example 2: Making Observations
  Once a distribution has been constructed, we can begin making observations.
  There are two methods for doing so. The first allows the user to directly set
  the observation count for a particular event:
  [source,c]
  