Introduction I recently wanted to port some python code to c++ (latency reasons) and I quickly realised it was not going to be a trivial port. It required some template metaprogramming trickery and in the process I found myself writing my first c++ concept 1.

Count data is commonly modelled using a generalised linear model with a Poisson likelihood, and this approach extends trivially to cases with multiple count data random variables, $Z_i\in\mathbb{Z}^{0+}, i=1\ldots N$ which are indepedent of one another, $Z_i\perp Z_j, \forall i,j$ because the likelihoods factor out nicely.

This is an example from chapter 5 (‘Basis Expansions and Regularisation’) of the excellent Elements of Statistical Learning book 1. It’s section 5.2.3 - ‘Phoneme Recognition’. The dataset 2 contains 1717 examples of periodogram data for different test subjects saying the letter combinations such as “ao” and “aa”.