The NYT has a travel feature on visiting the birthplace of Comte de Buffon, a contemporary and competitor to Carolus Linnaeus, who by this account played Hooke to Linnaeus' Newton. Of course, I (and probably most of my readers) know Buffon not for his work in biology and the natural world, but for the famous problem that bears his name and by some accounts gave rise to the area of geometric probability.

The Buffon needle problem is this: Suppose we drop a needle of length l onto a grid with lines spaced d units apart. What is the probability that the needle touches a grid line ? (let's assume for now that l <> d, the expression is more involved).

The Buffon's needle problem is a textbook example of the area of geometric probability: doing probability theory on entities defined geometrically. This is a harder thing than one might imagine: as Bertrand demonstrated much later on, the very notion of a uniform random sample can be called into question. He showed this by posing a simple question:

Draw an equilateral triangle and circumscribe a circle around it. Pick a chord at random. What is the probability that the chord length is longer than the side of the triangle ?

It turned out that the "Pick a chord at random" step leads to all kinds of problems. Bertrand presented three different equally reasonable ways of picking a chord, and showed that each of the three ways led to a different probability. At the time, this was viewed as a paradox: it seemed strange that what appeared to be a well defined question would lead to three different answers. Of course, the modern view is that there is no paradox, merely a different choice of measure space to work with in each case.

But if that's the case, is there a "natural" choice of measure ? One elegant way of thinking about this was to think of this as a physical process that must obey certain transformation invariants. This leads inexorably to notions like the Haar measure: can we assign a measure to subsets of a group that is invariant over left (or right) group actions ?

The Haar measure has practical utility for anyone working in geometry. Consider the problem of choosing a random rotation (this comes up in when doing Johnson-Lindenstrauss embeddings). In 2 dimensions, this is easy, since the space of rotations is the unit circle. However, in three dimensions or higher, it gets a bit trickier to define the correct measure to sample with respect to. Since rotations form a group, we really need a measure that is invariant under the group action, and a (unique upto scaling) Haar measure is guaranteed to exist in this case, yielding the desired measure to sample from (it's actually fairly easy to generate a random rotation algorithmically: the easiest is to populate a matrix with independent normally distributed random variables, and then orthogonalize it).

But it all goes back to a needle, a grid, and a frustrated naturalist. Happy new year !

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