Obviously Gelman and Nolan are smart and careful people. And we are discussing a well-regarded peer-reviewed article. So we don’t expect there is a major error. What we say is the abstraction they are using doesn’t match the physical abstraction I would pick. I pick a different one and I get different results. This is what I would like to discuss. Continue reading I still think you can manufacture an unfair coin→

One of the advantages of functional languages (such as R) is the ability to create and return functions “on the fly.” We will discuss one good use of this capability and what to look out for when creating functions in R. Continue reading How and why to return functions in R→

For more and more clients we have been using a nice coding pattern taught to us by Garrett Grolemund in his book Hands-On Programming with R: make a function that returns a list of functions. This turns out to be a classic functional programming techique: use closures to implement objects (terminology we will explain).

It is a pattern we strongly recommend, but with one caveat: it can leak references similar to the manner described in here. Once you work out how to stomp out the reference leaks the “function that returns a list of functions” pattern is really strong.

Having worked in finance I am a public fan of the Sharpe ratio. I have written about this here and here.

One thing I have often forgotten (driving some bad analyses) is: the Sharpe ratio isn’t appropriate for models of repeated events that already have linked mean and variance (such as Poisson or Binomial models) or situations where the variance is very small (with respect to the mean or expectation). These are common situations in a number of large scale online advertising problems (such as modeling the response rate to online advertisements or email campaigns).

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How sure are you that large margin implies low VC dimension (and good generalization error)? It is true. But even if you have taken a good course on machine learning you many have seen the actual proof (with all of the caveats and conditions). I worked through the literature proofs over the holiday and it took a lot of notes to track what is really going on in the derivation of the support vector machine.

Figure: the standard SVM margin diagram, this time with some un-marked data added.