An AI can build and try out statistical models using an open-ended generative grammar

I can’t comment on the details, especially as this sort of predictive regression problem isn’t the thing I typically work on, but I like the general idea of constructing models through some sort of generative grammar. It seems to me a big step forward from …

I can’t comment on the details, especially as this sort of predictive regression problem isn’t the thing I typically work on, but I like the general idea of constructing models through some sort of generative grammar. It seems to me a big step forward from the previous graphical-model paradigm in which the model is a static mixture of a bunch of conditional independence structures on a fixed set of variables. As I’ve written many times (for example, with Shalizi in our instant-classic paper, rejoinder here), I think discrete Bayesian model averaging is a poor model for science and a poor model for statistical inference. This open-ended approach smells right to me. It’s possible that all that horrible graphical model-averaging stuff was a necessary stage that statisticians and cognitive scientists had to go through on the way to models of generative grammar.

I feel so lucky to be around during this exciting era. Imagine…

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