A student came by interested in survey statistics and we go to talking about what an amazing person Leslie Kish must have been. We did some googling on it. Here are a few items we found:
http://projecteuclid.org/download/pdf_1/euclid.ss/1032209665
http://www.amstat.org/about/statisticiansinhistory/index.cfm?fuseaction=biosinfo&BioID=9
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Filed under statistics
http://sensitivecities.com/so-youd-like-to-make-a-map-using-python-EN.html#.VMKgbUfF-Yt
https://www.packtpub.com/books/content/working-geo-spatial-data-python
I just want the outlines of selected countries… that should be easy, right?
Filed under software engineering
I am flipping through yet another National Academy report this week. They know what hooks me. This time: What Research Says About Effective Instruction in Undergraduate Science and Engineering (2015). http://www.nap.edu/catalog/18687/reaching-students-what-research-says-about-effective-instruction-in-undergraduate
Lots of ideas for little changes to my class in here…
I mean, not exactly what I will do, but lots of inspiration.
Filed under science policy
A set of slides from a talk by Matthew Salgnik crossed my inbox recently, titled “Open and Reproducible Research: Goals, Obstacles, and Solutions”. Good stuff! I liked the *bonus points* in the Data-is-available section:
bonus points for releasing extra variables that are not need to reproduce specific analysis.
This gets at what I think is really the point of reproducible research. To make it faster and easier to make new knowledge.
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I wonder if this will be useful: Modeling Good Research Practices—Overview: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-1 http://www.ispor.org/workpaper/Modeling_Methods/Modeling_Good_Research_Practices_Overview-1.pdf
It has quite a lot of best practices!
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Filed under disease modeling
I’ve got a fun class going this quarter, on “artificial intelligence for health metricians”, and the course content mixed with some of the student interest has got me looking at the options for doing Gaussian process regression in Python. `PyMC2` has some nice stuff, but the `sklearn` version fits with the rest of my course examples more naturally, so I’m using that instead.
But `sklearn` doesn’t have the fanciest of fancy covariance functions implemented, and at IHME we have been down the road of the Matern covariance function for over five years now. It’s in `PyMC`, so I took a crack at mash-up. (Took a mash at a mash-up?) There is some room for improvement, but it is a start. If you need to do non-parametric regression for something that is differentiable more than once, but less than infinity times, you could try starting here: http://nbviewer.ipython.org/gist/aflaxman/af7bdb56987c50f3812b
p.s. Chris Fonnesbeck has some great notes on doing stuff like this and much more here: http://nbviewer.ipython.org/github/fonnesbeck/Bios366/blob/master/notebooks/Section5_1-Gaussian-Processes.ipynb
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…about interdisciplinary research, at that: http://www.nap.edu/catalog/11153/facilitating-interdisciplinary-research
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It’s not quite d3-broke-and-made-art quality, but I like the plot in this bug report: https://github.com/jakevdp/mpld3/issues/274#issuecomment-68576519
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Filed under dataviz, software engineering
PyMC2 has some tricky tricks for reducing function evaluations if possible. A question asked and answered on Stack Overflow investigates: http://stackoverflow.com/q/27714635/1935494 and I made a IPython Notebook with more details, too: http://nbviewer.ipython.org/gist/aflaxman/c07c5261bf22f6847098
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Here is a little trick for getting around a pesky initialization issue in PyMC2 models, asked and answers on Stack Overflow when thing were quiet around here: http://stackoverflow.com/a/27724637/1935494
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Filed under software engineering
Healthy Algorithms 