(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
Burak Bayramli writes:
I wanted to inform you on iPython Notebook technology – allowing markup, Python code to reside in one document. Someone ported one of your examples from ARM.
iPynb file is actually a live document, can be downloaded and reran locally, hence change of code on document means change of images, results. Graphs (as well as text output) which are generated by the code, are placed inside the document automatically. No more referencing image files seperately.
For now running notebooks locally require a notebook server, but that part can live “on the cloud” as part of an educational software. Viewers, such as nbviewer.ipython.org, do not even need that much, since all recent results of a notebook are embedded in the notebook itself.
A lot of people are excited about this; Also out of nowhere, Alfred P. Sloan Foundation dropped a $1.15 million grant on the developers of ipython which provided some extra energy on the project.
Cool. We’ll have to do that example in Stan too, especially now that we can easily fit a Gaussian process. It shouldn’t be too hard if we just discretize the arsenic and distance variables into something like 50 categories each.
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