In slow progress on my plan to to go through the examples from the OpenBUGS webpage and port them to PyMC, I offer you now Blockers, a random effects meta-analysis of clinical trials.
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Understanding the Elsevier Boycott
Hello Dear Readers,
Can someone help me quickly get up to speed on the Elsevier boycott? I’ve had a read through thecostofknowledge.com and even skimmed through Tim Gower’s statement of purpose. What I’m missing is what are the demands of this boycott? I’m delighted to have an excuse to refuse a request for refereeing, but how can my boycott be genuine about this if Elsevier has no way to make things right?
Filed under general
While searching for that Tukey quote
I was looking for a quote that was the topic of my last post, and I found it in the resources list for this very interesting organization, The Public Science Project. They have a 14 minutes video about their work which I recommend:
Filed under education
Tukey quote I half-remembered
I was trying to remember some quote by the exploratory data analysis master John Tukey yesterday, and I think this is it:
No catalog of techniques can convey a willingness to look for what can be seen, whether or not anticipated. Yet this is at the heart of exploratory data analysis. The graph paper—and transparencies—are there, not as a technique, but rather as a recognition that the picture-examining eye is the best finder we have of the wholly unanticipated.
It is from John W. Tukey, We Need Both Exploratory and Confirmatory, The American Statistician, Vol. 34, No. 1 (Feb., 1980), pp. 23-25.
I remembered a version about the visual cortex as a the most advance signal processing device, so maybe there is another version of this out there.
Filed under statistics
Automated Quality Assurance for Mobile Data Collection
I’m excited to call your attention to a paper that my co-author Ben Birnbaum is presenting next week at the ACM DEV conference:
- Benjamin Birnbaum, Brian DeRenzi, Abraham D. Flaxman, and Neal Lesh, Automated Quality Assurance for Mobile Data Collection
This research is about… well, the title says it pretty clearly. I’m interested in using our approach to detect surprises in data quality in all kinds of settings. Ben did the heavy lifting for this paper, so he deserves a lot of the congratulations that it has received the best paper award from the DEV 2012 program committee.
Congratulations, Ben!
Filed under global health
MCMC in Python: A random effects logistic regression example
I have had this idea for a while, to go through the examples from the OpenBUGS webpage and port them to PyMC, so that I can be sure I’m not going much slower than I could be, and so that people can compare MCMC samplers “apples-to-apples”. But its easy to have ideas. Acting on them takes more time.
So I’m happy that I finally found a little time to sit with Kyle Foreman and get started. We ported one example over, the “seeds” random effects logistic regression. It is a nice little example, and it also gave me a chance to put something in the ipython notebook, which I continue to think is a great way to share code.
Filed under MCMC, software engineering
Cool Tool
Here is a web tool that recently crossed my desk, an interactive map on the human cost of mountaintop removal. It is a mashup of a bunch of different data sources, all on a Google map, that all say it is not healthy to live near a mountaintop removal coal mine.
Filed under global health
