Speaker
Peter Latham
(UCL, UK)
Description
Both natural and artificial systems often exhibit a
surprising degree of statistical regularity. One such
regularity is Zipf's law. Originally formulated for word
frequency, Zipf's law has since been observed in a broad
range of phenomena, including city size, firm size, mutual
fund size, amino acid sequences, and neural activity. Partly
because it is so unexpected, a great deal of effort has gone
into explaining it. So far, almost all explanations are
either domain specific or require fine-tuning. Here we
propose an alternative explanation, which exploits the fact
that most real-world datasets can be understood as being
generated from a latent variable model. We show that data
generated from a such a model exhibits Zipf's law under very
mild conditions. We provide the theoretical underpinnings of
this result, illustrate it on words and neural data, and
point out examples of Zipf's law in the literature for which
we can identify a latent variable model.
