Update hierarchical-models.md

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@@ -125,7 +125,7 @@ Now let us introduce another level of abstraction: a global prototype that provi
 
 Compared with inferences in the previous example, this extra level of abstraction enables faster learning: more confidence in what each bag is like based on the same observed sample.  This is because all of the observed samples suggest a common prototype structure, with most of its weight on `blue` and the rest of the weight spread uniformly among the remaining colors.  Statisticians sometimes refer to this phenomenon of inference in hierarchical models as "sharing of statistical strength": it is as if the sample we observe for each bag also provides a weaker indirect sample relevant to the other bags.  In machine learning and cognitive science this phenomenon is often called *learning to learn* or *transfer learning.* Intuitively, knowing something about bags in general allows the learner to transfer knowledge gained from draws from one bag to other bags.  This example is analogous to seeing several examples of different subtypes of dogs and learning what features are in common to the more abstract basic-level dog prototype, independent of the more idiosyncratic features of particular dog subtypes.
 
-A particularly striking example of "sharing statistical strength" or "learning to learn" can be seen if we change the observed sample for bag 3 to have only two examples, one blue and one orange.  Replace the line `(equal? (draw-marbles 'bag-3 6) '(blue blue blue blue blue orange))` with `(equal? (draw-marbles 'bag-3 2) '(blue orange))` in each program above.  In a situation where we have no shared higher-order prototype structure, inference for bag-3 from these observations suggests that `blue` and `orange` are equally likely.  However, when we have inferred a shared higher-order prototype, then the inferences we make for bag 3 look much more like those we made before, with six observations (five blue, one orange), because the learned higher-order prototype tells us that blue is most likely to be highly represented in any bag regardless of which other colors (here, orange) may be seen with lower probability.
+A particularly striking example of "sharing statistical strength" or "learning to learn" can be seen if we change the observed sample for bag 3 to have only two examples, one blue and one orange.  Replace the line `(observe-bag 'bag-3 '(blue blue blue blue blue orange))` with `(observe-bag 'bag-3 '(blue orange))` in each program above.  In a situation where we have no shared higher-order prototype structure, inference for bag-3 from these observations suggests that `blue` and `orange` are equally likely.  However, when we have inferred a shared higher-order prototype, then the inferences we make for bag 3 look much more like those we made before, with six observations (five blue, one orange), because the learned higher-order prototype tells us that blue is most likely to be highly represented in any bag regardless of which other colors (here, orange) may be seen with lower probability.
 
 Learning about shared structure at a higher level of abstraction also supports inferences about new bags without observing *any* examples from that bag: a hypothetical new bag could produce any color, but is likely to have more blue marbles than any other color. We can imagine hypothetical, previously unseen, new subtypes of dogs that share the basic features of dogs with more familiar kinds but may differ in some idiosyncratic ways.