(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
After seeing this post by Matthew Wilson on a class of regression models called “factorization machines,” Aki writes:
In a typical machine learning way, this is called “machine”, but it would be also a useful mode structure in Stan to make linear models with interactions, but with a reduced number of parameters. With a fixed k, the rest of the parameters are continuous and it would be a first step toward the models Dunson talked in his presentation. To get this fast it may require a built-in function (to get a small expression tree), but for a moderately big datasets it’s not going to need all these algorithm mentioned in this posting.
Interesting, perhaps something for someone to work on. It requires some programming but not within core Stan code.
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