Last week we had a talk from Rodrigo Moreno-Serra on Universal health coverage, equity, and health outcomes. This research used instrumental variables to show that universal health coverage is good for health. One day I will understand instrumental variables—I think there should be a simple way to explain it to combinatorialists who know some epidemiology.
Monthly Archives: February 2014
It has been a while since I visited my pymc-examples repository, but I got a request there a few weeks ago about the feasibility of upgrading the Seeds Example of a random effects logistic regression model for PyMC3. It turns out that this was not very time consuming, which must mean I’m starting to understand the changes between PyMC2 and PyMC3.
Last week for IHME seminar, we heard from Kate Starbird about Crowds, crisis, and convergence: crowdsourcing in the context of disasters. It reminded me of the visual displays of quantitative information I hacked on after the 2010 earthquake in Haiti.
Did I ever tell you how the US State Department called to ask if they could use that graphic in a presentation? I thought it was a prank.
As often is the case, a recording of the talk is available online.
My grad school colleague Jure Leskovec was at UW recently, talking about a cool new model for clusters in random graphs. It was part generative model, and part statistical model, and made me miss my grad school days when I spent long hours proving theorems about such things. This statistical part of his model has a very forced acronym: BigCLAM, and also a software implementation: https://github.com/snap-stanford/snap/tree/master/examples/bigclam
Maybe one day I’ll get to do some applied network research for global health, and have a chance to use this.
IHME Seminar: Unifying the Counterfactual and Graphical Approaches to Causality via Single World Intervention Graphs (SWIGs)
Thomas Richardson gave a recent seminar at IHME about how the potential outcomes crowd can make sense of graphical models and vice versa. It also has a CSSS working paper to complement it, a trend in our recent seminars: http://www.csss.washington.edu/Papers/wp128.pdf
I’ve been reading about Sequential Monte Carlo recently, and I think it will fit well into the PyMC3 framework. I will give it a try when I have a free minute, but maybe someone else will be inspired to try it first. This paper includes some pseudocode.
IHME Seminar: Bayesian reconstruction: estimating past populations and vital rates by age with uncertainty in a variety of data-quality contexts
A recent IHME seminar by Mark Wheldon described a Bayesian approach to estimating past populations and vital rates by age. I like this stuff. The talk is online, and there is a CSSS working paper on it, too: http://www.csss.washington.edu/Papers/wp117.pdf
Journal Club: Repeat Bone Mineral Density Screening and Prediction of Hip and Major Osteoporotic Fracture
Last week we read Repeat Bone Mineral Density Screening and Prediction of Hip and Major Osteoporotic Fracture by Berry et al. It argues that repeat screening does not improve predictions. But I think the world needs a better way to measure the quality of predictions like these. Area-under-the-curve doesn’t cut it when you are predicting the unpredictable.