Transitioning to Stan

April 14, 2014
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(This article was first published on Statistical Modeling, Causal Inference, and Social Science » R, and kindly contributed to R-bloggers)

Kevin Cartier writes:

I’ve been happily using R for a number of years now and recently came across Stan. Looks big and powerful, so I’d like to pick an appropriate project and try it out. I wondered if you could point me to a link or document that goes into the motivation for this tool (aside from the Stan user doc)? What I’d like to understand is, at what point might you look at an emergent R project and advise, “You know, that thing you’re trying to do would be a whole lot easier/simpler/more straightforward to implement with Stan.” (or words to that effect).

My reply: For my collaborators in political science, Stan has been most useful for models where the data set is not huge (e.g., we might have 10,000 data points or 50,000 data points but not 10 million) but where the model is somewhat complex (for example, a model with latent time series structure). The point is that the model has enough parameters and uncertainty that you’ll want to do full Bayes (rather than some sort of point estimate). At that point, Stan is a winner compared to programming one’s own Monte Carlo algorithm.

We (the Stan team) should really prepare a document with a bunch of examples where Stan is a win, in one way or another. But of course preparing such a document takes work, which we’d rather spend on improving Stan (or on blogging…)

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