I found some archival videos of Donald Knuth teaching literate programming in his mathematical writing class in 1987:
Lots of other promising stuff on the Stanford page that links to it: http://scpd.stanford.edu/free-stuff/engineering-archives/donald-e-knuth-lectures
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.
An old adage when writing research papers is “put it in a figure”. If there is one thing that I want the reader to know when they put my paper down, then I try to put it in a beautiful figure, with a complete explanation in the caption. I saw the extension of this rule to talks recently, and I’m going to try it out myself: if there is one thing you want your audience to remember when they leave your talk, put it in a movie.
Here is the movie that taught me this lesson:
And here is a blog post by one of the video creators, to tell you more about what you’re seeing.
There has been some interesting traffic on the PyMC mailing list lately. It seems that there is a common trouble with the “Adaptive Metropolis” step method, and it’s failure to converge. I’ve been quite impressed with this approach, and I haven’t had the problems that others reported, so I started wondering: Have I been lucky? Have I been not looking carefully?
I decided to do some experiments to make Metropolis and Adaptive Metropolis shine, and since I’m an aspiring math filmmaker these days, I made my experiments into movies.
I consider the Metropolis step method the essence of MCMC. You have a particular point in parameter space, and you tentatively perturb it to a new random point, and then decide to accept or reject this new point with a carefully designed probability, so that the stationary distribution of the Markov chain is something you’re interested in. It’s magic, but it’s good, simple magic.
Here is the simplest example I could come up with, sampling from a uniform distribution on the unit square in the plane using Metropolis steps. Any self-respecting step method can sample from the unit square in two dimensions!
Since Seattle rarely sees snow the city gets quite shut down when it does happen. We have a few inches on the ground today, so despite the beautiful sun, UW has suspended operations. Fine for me, I like the change of scenery. Somehow, these snowy days have got me watching a lot of online videos. First of all, a movie recommendation: In the loop, a political satire, which is the genre that Jessi and I agree on the most.
But for those of you looking for a shorter diversion and/or something healthyalgorithms related, I call your attention first to Google Refine Screencasts and second to a list of the 20 must-see CS TED talks from the mastersincomputerscience blog.
On the 20 must-see list, Torsten Reil’s talk caught my eye. He has built these human models with really lifelike kinetics using serious physics and biology, and then has 15 minutes of his talk devoted to showing what happens when he pushes them around. My caricature of a hard-AI researcher is a mad scientist building a sentient computer to be their only friend, so the way Reil has created these lifelike virtual people just to beat the up and make fun of their dance moves, that exceeds my expectations.
I had a break yesterday to see one of those “summer blockbusters”, a spy flick staring Angelina Jolie called Salt. It had some good explosions and good action, but overall it was so outrageously terrible that I will reveal the entire cloak-and-dagger twist to complain. (spoiler ahead) Continue reading
Whoops, I got busy again and didn’t have time to make new pictures of TFR vs HDI for Rif and Tanja, let alone fix the Bayes factor estimation code or implement the nested sampling version (which I think will be the cool way to estimate evidence). But coming soon: How MCMC is tying my new work in Health Metrics to my education in Operations Research. That will be in two weeks, at best.
Until then, here is some light reading to get ready for a big week of US healthcare reform debate: Get Sick, Get Out, a survey conducted by lawyers interested in catastrophic medical payments and their connection to housing forclosures. It’s 40 pages long, but it’s in legal-journal format, where they have like 10 words per page if you skip the footnotes. From the abstract:
Half of all respondents (49%) indicated that their foreclosure was caused in part by a medical problem, including illness or injuries (32%), unmanageable medical bills (23%), lost work due to a medical problem (27%), or caring for sick family members (14%).
I’m excited for the next week of healthcare reform debates. When my most jaded friends are forwarding me Moveon.org videos (and I’m listening to 4 minutes of recent REM), I know something unusual is going on.
Happy labor day weekend!